
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

import math
from scipy.stats import norm, binom, geom, poisson # Distributions
from scipy.stats import zscore, ttest_1samp, ttest_ind, ttest_rel # Z-Test & T-Test
from scipy.stats import chisquare, chi2_contingency, chi2 # ChiSqured Test
from scipy.stats import f_oneway, kruskal, shapiro, levene, ks_2samp # Anova Test
from scipy.stats import spearmanr # Co-Relation Test

from statsmodels.graphics.gofplots import qqplot

sns.set_theme(style="darkgrid")


walmart_raw = pd.read_csv("./walmart_data.csv")
walmart_raw.head(5)


walmart_raw.shape

(550068, 10)


walmart_raw.size

5500680


walmart_raw.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 550068 entries, 0 to 550067
Data columns (total 10 columns):
 #   Column                      Non-Null Count   Dtype 
---  ------                      --------------   ----- 
 0   User_ID                     550068 non-null  int64 
 1   Product_ID                  550068 non-null  object
 2   Gender                      550068 non-null  object
 3   Age                         550068 non-null  object
 4   Occupation                  550068 non-null  int64 
 5   City_Category               550068 non-null  object
 6   Stay_In_Current_City_Years  550068 non-null  object
 7   Marital_Status              550068 non-null  int64 
 8   Product_Category            550068 non-null  int64 
 9   Purchase                    550068 non-null  int64 
dtypes: int64(5), object(5)
memory usage: 42.0+ MB


walmart_raw.isnull().sum()

User_ID                       0
Product_ID                    0
Gender                        0
Age                           0
Occupation                    0
City_Category                 0
Stay_In_Current_City_Years    0
Marital_Status                0
Product_Category              0
Purchase                      0
dtype: int64


walmart_raw.columns

Index(['User_ID', 'Product_ID', 'Gender', 'Age', 'Occupation', 'City_Category',
       'Stay_In_Current_City_Years', 'Marital_Status', 'Product_Category',
       'Purchase'],
      dtype='object')


def gender_convert(x):
    return 1 if(x=='M') else 0
walmart_raw['gender'] = walmart_raw.apply(lambda row: gender_convert(row['Gender']),axis=1)

def gender_rename(x):
    return "Male" if(x=='M') else ("Female" if(x=='F') else "")
walmart_raw['Gender'] = walmart_raw.apply(lambda row: gender_rename(row['Gender']),axis=1)

def matial_status_convert(x):
    return 'Single' if(x==0) else 'Partnered'
walmart_raw['marital_status'] = walmart_raw.apply(lambda row: matial_status_convert(row['Marital_Status']),axis=1)

def age_convert(x):
    if ('+' in x):
        return int(x.split('+')[0])
    if ('-' in x):
        return (int(x.split('-')[0])+int(x.split('-')[1]))/2
walmart_raw['age'] = walmart_raw.apply(lambda row: age_convert(row['Age']),axis=1)

def purchase_categorise(df):
    df["Purchase_group"] = pd.cut(x=df['Purchase'], bins=[0,100,500,1000,2500,5000,7500,10000,12500,15000,17500,20000,22500,25000],  
                    labels=["0-100", "101-500", "501-1K", "1K-2.5K", "2.5K-5K", "5K-7.5K", "7.5K-10K", "10K-12.5K", "12.5K-15K", "15K-17.5K", "17.5K-20K", "20K-22.5K", "22.5K-25K"])
    df['Purchase_group'] = df['Purchase_group'].astype('category')
    return df
walmart_raw = purchase_categorise(walmart_raw)


walmart_raw = walmart_raw[['User_ID', 'Gender', 'gender', 'Marital_Status', 'marital_status', 'Age', 'age', 'City_Category',
       'Stay_In_Current_City_Years', 'Occupation', 'Product_ID',  'Product_Category',
       'Purchase', 'Purchase_group']]


walmart_raw.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 550068 entries, 0 to 550067
Data columns (total 14 columns):
 #   Column                      Non-Null Count   Dtype   
---  ------                      --------------   -----   
 0   User_ID                     550068 non-null  int64   
 1   Gender                      550068 non-null  object  
 2   gender                      550068 non-null  int64   
 3   Marital_Status              550068 non-null  int64   
 4   marital_status              550068 non-null  object  
 5   Age                         550068 non-null  object  
 6   age                         550068 non-null  float64 
 7   City_Category               550068 non-null  object  
 8   Stay_In_Current_City_Years  550068 non-null  object  
 9   Occupation                  550068 non-null  int64   
 10  Product_ID                  550068 non-null  object  
 11  Product_Category            550068 non-null  int64   
 12  Purchase                    550068 non-null  int64   
 13  Purchase_group              550068 non-null  category
dtypes: category(1), float64(1), int64(6), object(6)
memory usage: 55.1+ MB


def check_outlier(df, x):
    Q1 = df[x].quantile(0.25)
    Q3 = df[x].quantile(0.75)
    IQR = Q3 - Q1
    lower = Q1 - 1.5*IQR
    upper = Q3 + 1.5*IQR
    lower_outlier = df[x][df[x] < lower]
    upper_outlier = df[x][df[x] > upper]

    return {
        'lower': {
            'list': lower_outlier,
            'length': len(lower_outlier)
        },
        'upper': {
            'list': upper_outlier,
            'length': len(upper_outlier)
        }}


for i in walmart_raw[['User_ID', 'age', 'Occupation', 'Product_Category', 'Purchase']].columns:
    # print(i)
    outlier = check_outlier(walmart_raw, i)
    print("{} : \n\t'Lower Outliers': {} \n\t'HIgher Outliers': {} \n\t'Mean - Median': {}\n".format(i, outlier['lower']['length'], outlier['upper']['length'], round(walmart_raw[i].mean()-walmart_raw[i].median(),4)))

User_ID : 
	'Lower Outliers': 0 
	'HIgher Outliers': 0 
	'Mean - Median': -48.1576

age : 
	'Lower Outliers': 15102 
	'HIgher Outliers': 0 
	'Mean - Median': 3.752

Occupation : 
	'Lower Outliers': 0 
	'HIgher Outliers': 0 
	'Mean - Median': 1.0767

Product_Category : 
	'Lower Outliers': 0 
	'HIgher Outliers': 4153 
	'Mean - Median': 0.4043

Purchase : 
	'Lower Outliers': 0 
	'HIgher Outliers': 2677 
	'Mean - Median': 1216.9687


plt.figure(figsize=(18,5)).suptitle(" Walmart Outliers")
plt.subplot(1,3,1)
sns.boxplot(walmart_raw, y='age')
plt.subplot(1,3,2)
sns.boxplot(walmart_raw, y='Product_Category')
plt.subplot(1,3,3)
sns.boxplot(walmart_raw, y='Purchase')
plt.show()


walmart_raw['Gender'] = walmart_raw['Gender'].astype('category')
walmart_raw['marital_status'] = walmart_raw['marital_status'].astype('category')
walmart_raw['Age'] = walmart_raw['Age'].astype('category')
walmart_raw['City_Category'] = walmart_raw['City_Category'].astype('category')
walmart_raw['Stay_In_Current_City_Years'] = walmart_raw['Stay_In_Current_City_Years'].astype('category')
# walmart_raw['Occupation'] = walmart_raw['Occupation'].astype('category')
# walmart_raw['Product_Category'] = walmart_raw['Product_Category'].astype('category')
walmart_raw['User_ID'] = walmart_raw['User_ID'].astype('object')
walmart_raw.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 550068 entries, 0 to 550067
Data columns (total 14 columns):
 #   Column                      Non-Null Count   Dtype   
---  ------                      --------------   -----   
 0   User_ID                     550068 non-null  object  
 1   Gender                      550068 non-null  category
 2   gender                      550068 non-null  int64   
 3   Marital_Status              550068 non-null  int64   
 4   marital_status              550068 non-null  category
 5   Age                         550068 non-null  category
 6   age                         550068 non-null  float64 
 7   City_Category               550068 non-null  category
 8   Stay_In_Current_City_Years  550068 non-null  category
 9   Occupation                  550068 non-null  int64   
 10  Product_ID                  550068 non-null  object  
 11  Product_Category            550068 non-null  int64   
 12  Purchase                    550068 non-null  int64   
 13  Purchase_group              550068 non-null  category
dtypes: category(6), float64(1), int64(5), object(2)
memory usage: 36.7+ MB


walmart_raw[['User_ID', 'Gender', 'marital_status', 'Age', 'City_Category',
       'Stay_In_Current_City_Years', 'Product_ID']].describe()


walmart_raw[['age', 'Occupation', 'Product_Category', 'Purchase']].describe()


walmart_raw.nunique()

User_ID                        5891
Gender                            2
gender                            2
Marital_Status                    2
marital_status                    2
Age                               7
age                               7
City_Category                     3
Stay_In_Current_City_Years        5
Occupation                       21
Product_ID                     3631
Product_Category                 20
Purchase                      18105
Purchase_group                   13
dtype: int64


walmart_raw[['age', 'Occupation', 'Product_Category', 'Purchase']].mean()

age                   34.251962
Occupation             8.076707
Product_Category       5.404270
Purchase            9263.968713
dtype: float64


walmart_raw[['age', 'Occupation', 'Product_Category', 'Purchase']].median()

age                   30.5
Occupation             7.0
Product_Category       5.0
Purchase            8047.0
dtype: float64


for i in walmart_raw.columns:
  print(i,':',walmart_raw[i].mode()[0])

User_ID : 1001680
Gender : Male
gender : 1
Marital_Status : 0
marital_status : Single
Age : 26-35
age : 30.5
City_Category : B
Stay_In_Current_City_Years : 1
Occupation : 4
Product_ID : P00265242
Product_Category : 5
Purchase : 7011
Purchase_group : 5K-7.5K


walmart_raw['User_ID'].unique()

array([1000001, 1000002, 1000003, ..., 1004113, 1005391, 1001529],
      dtype=object)


walmart_raw['User_ID'].value_counts()

1001680    1026
1004277     979
1001941     898
1001181     862
1000889     823
           ... 
1002690       7
1002111       7
1005810       7
1004991       7
1000708       6
Name: User_ID, Length: 5891, dtype: int64


walmart_raw['User_ID'].describe()

count      550068
unique       5891
top       1001680
freq         1026
Name: User_ID, dtype: int64


walmart_raw['User_ID'].mode()[0]

1001680


walmart_raw['User_ID'].value_counts(normalize=True)*100

1001680    0.186522
1004277    0.177978
1001941    0.163253
1001181    0.156708
1000889    0.149618
             ...   
1002690    0.001273
1002111    0.001273
1005810    0.001273
1004991    0.001273
1000708    0.001091
Name: User_ID, Length: 5891, dtype: float64


walmart_raw['Gender'].unique()

['Female', 'Male']
Categories (2, object): ['Female', 'Male']


walmart_raw['Gender'].value_counts()

Male      414259
Female    135809
Name: Gender, dtype: int64


walmart_raw['Gender'].value_counts(normalize=True)*100

Male      75.310507
Female    24.689493
Name: Gender, dtype: float64


walmart_raw['Gender'].describe()

count     550068
unique         2
top         Male
freq      414259
Name: Gender, dtype: object


walmart_raw['Gender'].mode()[0]

'Male'


walmart_raw.groupby('Gender')["Occupation"].describe()


sns.heatmap(pd.crosstab(walmart_raw['Gender'], walmart_raw['Gender'], normalize='all'),annot=True)
plt.title('Probabbility of Gender ∩ Gender', fontsize=12)

Text(0.5, 1.0, 'Probabbility of Gender ∩ Gender')


plt.figure(figsize=(15,5)).suptitle("Walmart Gender Dashboard",fontsize=14)

plt.subplot(1, 2, 1)
plt.pie(walmart_raw['Gender'].value_counts().values,labels = walmart_raw['Gender'].value_counts().index,radius = 1.3,autopct = '%1.2f%%',) # type: ignore

plt.subplot(1, 2
, 2)
sns.countplot(walmart_raw, x='Gender')
plt.ylabel('', fontsize=12)
plt.xlabel('Gender', fontsize=13)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)

plt.show()


walmart_raw['marital_status'].unique()

['Single', 'Partnered']
Categories (2, object): ['Partnered', 'Single']


walmart_raw['marital_status'].value_counts()

Single       324731
Partnered    225337
Name: marital_status, dtype: int64


walmart_raw['marital_status'].value_counts(normalize=True)*100

Single       59.034701
Partnered    40.965299
Name: marital_status, dtype: float64


walmart_raw['marital_status'].describe()

count     550068
unique         2
top       Single
freq      324731
Name: marital_status, dtype: object


walmart_raw['marital_status'].mode()[0]

'Single'


walmart_raw.groupby('Gender')["marital_status"].describe()


pd.crosstab(walmart_raw['marital_status'], walmart_raw['Gender'], normalize=True, margins=True)*100


pd.crosstab(walmart_raw['marital_status'], walmart_raw['Gender'], normalize='index', margins=True)*100


pd.crosstab(walmart_raw['marital_status'], walmart_raw['Gender'], normalize='columns', margins=True)*100


sns.heatmap(pd.crosstab(walmart_raw['Gender'], walmart_raw['marital_status'], normalize='all'),annot=True)
plt.title('Probabbility of Gender ∩ Marital Status', fontsize=12)

Text(0.5, 1.0, 'Probabbility of Gender ∩ Marital Status')


plt.figure(figsize=(15,5)).suptitle("Walmart Marital Status Dashboard",fontsize=14)

plt.subplot(1, 3, 1)
plt.pie(walmart_raw['marital_status'].value_counts().values,labels = walmart_raw['marital_status'].value_counts().index,radius = 1.3,autopct = '%1.2f%%',) # type: ignore

plt.subplot(1, 3, 2)
sns.countplot(walmart_raw, x='marital_status')
plt.ylabel('', fontsize=12)
plt.xlabel('Marital Status', fontsize=11)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)

plt.subplot(1, 3, 3)
sns.countplot(walmart_raw, x='marital_status', hue='Gender')
plt.ylabel('Count', fontsize=12)
plt.xlabel('Marital Status', fontsize=11)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)
plt.legend(borderaxespad=0, ncol=1)

plt.show()


(pd.crosstab(walmart_raw['marital_status'], walmart_raw['Gender'], normalize=True)*100).plot(kind='bar')
plt.title('Probability of Gender & Marital Status', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=10)
plt.legend(borderaxespad=2, ncol=1)
plt.show()


(pd.crosstab(walmart_raw['marital_status'], walmart_raw['Gender'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of Gender for given Marital Status', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=10)
plt.legend(bbox_to_anchor=(1.02, 1), loc='upper center', borderaxespad=3)
plt.show()


(pd.crosstab(walmart_raw['Gender'], walmart_raw['marital_status'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of Marital Status for given Gender', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=10)
plt.legend(bbox_to_anchor=(1.02, 1), loc='upper center', borderaxespad=3)
plt.show()


pd.crosstab(walmart_raw['marital_status'], walmart_raw['Gender'], normalize="index").plot(kind='pie', subplots=True, figsize=(12,4), labeldistance=None, fontsize=10, legend=None, autopct = '%1.2f%%')
plt.legend(borderaxespad=-1, ncol=3)
plt.show()
pd.crosstab(walmart_raw['Gender'], walmart_raw['marital_status'], normalize="index").plot(kind='pie', subplots=True, figsize=(8,4), labeldistance=None, fontsize=10, legend=None, autopct = '%1.2f%%')
plt.legend(borderaxespad=-1, ncol=3)
plt.show()


walmart_raw['Age'].unique()

['0-17', '55+', '26-35', '46-50', '51-55', '36-45', '18-25']
Categories (7, object): ['0-17', '18-25', '26-35', '36-45', '46-50', '51-55', '55+']


walmart_raw['Age'].value_counts()[:5]

26-35    219587
36-45    110013
18-25     99660
46-50     45701
51-55     38501
Name: Age, dtype: int64


walmart_raw['Age'].value_counts(normalize=True)[:5]*100

26-35    39.919974
36-45    19.999891
18-25    18.117760
46-50     8.308246
51-55     6.999316
Name: Age, dtype: float64


walmart_raw['Age'].describe()

count     550068
unique         7
top        26-35
freq      219587
Name: Age, dtype: object


walmart_raw['age'].mean()

34.251961575659735


walmart_raw['age'].median()

30.5


walmart_raw['Age'].mode()[0]

'26-35'


walmart_raw.groupby('Gender')["Age"].describe()


check_outlier(walmart_raw, 'age')['upper']

{'list': Series([], Name: age, dtype: float64), 'length': 0}


check_outlier(walmart_raw, 'age')['lower']

{'list': 0         8.5
 1         8.5
 2         8.5
 3         8.5
 85        8.5
          ... 
 549904    8.5
 550012    8.5
 550024    8.5
 550035    8.5
 550046    8.5
 Name: age, Length: 15102, dtype: float64,
 'length': 15102}


pd.crosstab(walmart_raw['Gender'], walmart_raw['age'], normalize=True, margins=True)*100


pd.crosstab(walmart_raw['Gender'], walmart_raw['age'], normalize='index', margins=True)*100


pd.crosstab(walmart_raw['Gender'], walmart_raw['age'], normalize='columns', margins=True)*100


sns.heatmap(pd.crosstab(walmart_raw['Age'], walmart_raw['Gender'], normalize='all'),annot=True)
plt.title('Probabbility of Age ∩ Gender', fontsize=12)

Text(0.5, 1.0, 'Probabbility of Age ∩ Gender')


plt.figure(figsize=(15,10)).suptitle("Walmart Age Dashboard",fontsize=14)

plt.subplot(2, 2, 1)
plt.pie(walmart_raw['Age'].value_counts().values,labels = walmart_raw['Age'].value_counts().index,radius = 1.3,autopct = '%1.2f%%') # type: ignore

plt.subplot(3, 2, 2)
sns.countplot(walmart_raw, x='Age', hue='Gender')
plt.title('No of Gender Purchase vs Age', fontsize=12)
plt.ylabel('Count', fontsize=12)
plt.xlabel('', fontsize=11)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)

plt.subplot(3, 2, 4)
sns.countplot(walmart_raw, x='Age')
plt.ylabel('Count', fontsize=12)
plt.xlabel('', fontsize=11)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)

plt.show()


(pd.crosstab(walmart_raw['Age'], walmart_raw['Gender'], normalize=True)*100).plot(kind='bar')
plt.title('Probability of Gender & Age Group', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=10)
plt.legend(borderaxespad=2, ncol=1)
plt.show()


(pd.crosstab(walmart_raw['Age'], walmart_raw['Gender'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of Gender for given Age Group', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=10)
plt.legend(borderaxespad=1, ncol=3)
plt.show()


(pd.crosstab(walmart_raw['Gender'], walmart_raw['Age'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of Age Group for given Gender', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=10)
plt.legend(bbox_to_anchor=(1.02, 1), loc='upper center', borderaxespad=5)

plt.show()


pd.crosstab(walmart_raw['Age'], walmart_raw['Gender'], normalize="index").plot(kind='pie', subplots=True, figsize=(15,4), labeldistance=None, fontsize=10, legend=None, autopct = '%1.2f%%')
plt.legend(borderaxespad=-1, ncol=7)
plt.show()
pd.crosstab(walmart_raw['Gender'], walmart_raw['Age'], normalize="index").plot(kind='pie', subplots=True, figsize=(20,5), labeldistance=None, fontsize=10, legend=None, autopct = '%1.2f%%')
plt.legend(borderaxespad=-1, ncol=3)
plt.show()


walmart_raw['City_Category'].unique()

['A', 'C', 'B']
Categories (3, object): ['A', 'B', 'C']


walmart_raw['City_Category'].value_counts()

B    231173
C    171175
A    147720
Name: City_Category, dtype: int64


walmart_raw['City_Category'].value_counts(normalize=True)*100

B    42.026259
C    31.118880
A    26.854862
Name: City_Category, dtype: float64


walmart_raw['City_Category'].describe()

count     550068
unique         3
top            B
freq      231173
Name: City_Category, dtype: object


walmart_raw['City_Category'].mode()[0]

'B'


walmart_raw.groupby('Gender')["City_Category"].describe()


pd.crosstab(walmart_raw['City_Category'], walmart_raw['Gender'], normalize=True, margins=True)*100


pd.crosstab(walmart_raw['City_Category'], walmart_raw['Gender'], normalize='index', margins=True)*100


pd.crosstab(walmart_raw['City_Category'], walmart_raw['Gender'], normalize='columns', margins=True)*100


sns.heatmap(pd.crosstab(walmart_raw['City_Category'], walmart_raw['Gender'], normalize='all'),annot=True)
plt.title('Probabbility of Gender ∩ City Category', fontsize=12)

Text(0.5, 1.0, 'Probabbility of Gender ∩ City Category')


plt.figure(figsize=(15,10)).suptitle("Walmart City Category Dashboard",fontsize=14)

plt.subplot(2, 2, 1)
plt.pie(walmart_raw['City_Category'].value_counts().values,labels = walmart_raw['City_Category'].value_counts().index,radius = 1.3,autopct = '%1.2f%%') # type: ignore

plt.subplot(3, 2, 2)
sns.countplot(walmart_raw, x='City_Category')
plt.ylabel('Count', fontsize=12)
plt.xlabel('', fontsize=11)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)

plt.subplot(3, 2, 4)
sns.countplot(walmart_raw, x='City_Category', hue='Gender')
plt.ylabel('Count', fontsize=12)
plt.xlabel('City Category', fontsize=11)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)
plt.legend(borderaxespad=1, ncol=1)

plt.show()


(pd.crosstab(walmart_raw['City_Category'], walmart_raw['Gender'], normalize=True)*100).plot(kind='bar')
plt.title('Probability of Gender & City Category', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=12)
plt.legend(borderaxespad=2, ncol=1)
plt.show()


(pd.crosstab(walmart_raw['City_Category'], walmart_raw['Gender'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of Gender for given City Category', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=12)
plt.legend(borderaxespad=1, ncol=1)
plt.show()


(pd.crosstab(walmart_raw['Gender'], walmart_raw['City_Category'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of City Category for given Gender', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=12)
plt.legend(bbox_to_anchor=(1.02, 1), loc='upper center', borderaxespad=5, ncol=2)

plt.show()


pd.crosstab(walmart_raw['City_Category'], walmart_raw['Gender'], normalize="index").plot(kind='pie', subplots=True, figsize=(15,5), labeldistance=None, fontsize=10, legend=None, autopct = '%1.2f%%')
plt.legend(borderaxespad=-1, ncol=7)
plt.show()
pd.crosstab(walmart_raw['Gender'], walmart_raw['City_Category'], normalize="index").plot(kind='pie', subplots=True, figsize=(15,5), labeldistance=None, fontsize=10, legend=None, autopct = '%1.2f%%')
plt.legend(borderaxespad=-1, ncol=3)
plt.show()


walmart_raw['Stay_In_Current_City_Years'].unique()

['2', '4+', '3', '1', '0']
Categories (5, object): ['0', '1', '2', '3', '4+']


walmart_raw['Stay_In_Current_City_Years'].value_counts()

1     193821
2     101838
3      95285
4+     84726
0      74398
Name: Stay_In_Current_City_Years, dtype: int64


walmart_raw['Stay_In_Current_City_Years'].value_counts(normalize=True)*100

1     35.235825
2     18.513711
3     17.322404
4+    15.402823
0     13.525237
Name: Stay_In_Current_City_Years, dtype: float64


walmart_raw['Stay_In_Current_City_Years'].describe()

count     550068
unique         5
top            1
freq      193821
Name: Stay_In_Current_City_Years, dtype: object


walmart_raw['Stay_In_Current_City_Years'].mode()[0]

'1'


walmart_raw.groupby('Gender')["Stay_In_Current_City_Years"].describe()


pd.crosstab(walmart_raw['Stay_In_Current_City_Years'], walmart_raw['Gender'], normalize=True, margins=True)*100


pd.crosstab(walmart_raw['Stay_In_Current_City_Years'], walmart_raw['Gender'], normalize='index', margins=True)*100


pd.crosstab(walmart_raw['Stay_In_Current_City_Years'], walmart_raw['Gender'], normalize='columns', margins=True)*100


sns.heatmap(pd.crosstab(walmart_raw['Stay_In_Current_City_Years'], walmart_raw['Gender'], normalize='all'),annot=True)
plt.title('Probabbility of Gender ∩ Stay In Current City Years', fontsize=12)

Text(0.5, 1.0, 'Probabbility of Gender ∩ Stay In Current City Years')


plt.figure(figsize=(15,10)).suptitle("Walmart Stay In Current City Years Dashboard",fontsize=14)

plt.subplot(2, 2, 1)
plt.pie(walmart_raw['Stay_In_Current_City_Years'].value_counts().values,labels = walmart_raw['Stay_In_Current_City_Years'].value_counts().index,radius = 1.3,autopct = '%1.2f%%') # type: ignore

plt.subplot(3, 2, 2)
sns.countplot(walmart_raw, x='Stay_In_Current_City_Years')
plt.ylabel('Count', fontsize=12)
plt.xlabel('', fontsize=11)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)

plt.subplot(3, 2, 4)
sns.countplot(walmart_raw, x='Stay_In_Current_City_Years', hue='Gender')
plt.ylabel('Count', fontsize=12)
plt.xlabel('Stay In Current City Years Level', fontsize=11)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)
plt.legend(borderaxespad=1, ncol=1)

plt.show()


(pd.crosstab(walmart_raw['Stay_In_Current_City_Years'], walmart_raw['Gender'], normalize=True)*100).plot(kind='bar')
plt.title('Probability of Gender & Stay In Current City Years', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=12)
plt.legend(borderaxespad=2, ncol=1)
plt.show()


(pd.crosstab(walmart_raw['Stay_In_Current_City_Years'], walmart_raw['Gender'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of Gender for given Stay In Current City Years', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=12)
plt.legend(borderaxespad=1, ncol=1)
plt.show()


(pd.crosstab(walmart_raw['Gender'], walmart_raw['Stay_In_Current_City_Years'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of Stay In Current City Years for given Gender', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=12)
plt.legend(bbox_to_anchor=(1.02, 1), loc='upper center', borderaxespad=5, ncol=2)

plt.show()


pd.crosstab(walmart_raw['Stay_In_Current_City_Years'], walmart_raw['Gender'], normalize="index").plot(kind='pie', subplots=True, figsize=(15,5), labeldistance=None, fontsize=10, legend=None, autopct = '%1.2f%%')
plt.legend(borderaxespad=-1, ncol=7)
plt.show()
pd.crosstab(walmart_raw['Gender'], walmart_raw['Stay_In_Current_City_Years'], normalize="index").plot(kind='pie', subplots=True, figsize=(15,5), labeldistance=None, fontsize=10, legend=None, autopct = '%1.2f%%')
plt.legend(borderaxespad=-1, ncol=3)
plt.show()


walmart_raw['Occupation'].unique()

array([10, 16, 15,  7, 20,  9,  1, 12, 17,  0,  3,  4, 11,  8, 19,  2, 18,
        5, 14, 13,  6], dtype=int64)


walmart_raw['Occupation'].value_counts()

4     72308
0     69638
7     59133
1     47426
17    40043
20    33562
12    31179
14    27309
2     26588
16    25371
6     20355
3     17650
10    12930
5     12177
15    12165
11    11586
19     8461
13     7728
18     6622
9      6291
8      1546
Name: Occupation, dtype: int64


walmart_raw['Occupation'].value_counts(normalize=True)*100

4     13.145284
0     12.659889
7     10.750125
1      8.621843
17     7.279645
20     6.101427
12     5.668208
14     4.964659
2      4.833584
16     4.612339
6      3.700452
3      3.208694
10     2.350618
5      2.213726
15     2.211545
11     2.106285
19     1.538173
13     1.404917
18     1.203851
9      1.143677
8      0.281056
Name: Occupation, dtype: float64


walmart_raw['Occupation'].describe()

count    550068.000000
mean          8.076707
std           6.522660
min           0.000000
25%           2.000000
50%           7.000000
75%          14.000000
max          20.000000
Name: Occupation, dtype: float64


walmart_raw['Occupation'].mean()

8.076706879876669


walmart_raw['Occupation'].median()

7.0


walmart_raw['Occupation'].mode()[0]

4


walmart_raw.groupby('Gender')["Occupation"].describe()


check_outlier(walmart_raw, 'Occupation')['upper']

{'list': Series([], Name: Occupation, dtype: int64), 'length': 0}


check_outlier(walmart_raw, 'Occupation')['lower']

{'list': Series([], Name: Occupation, dtype: int64), 'length': 0}


pd.crosstab(walmart_raw['Occupation'], walmart_raw['Gender'], normalize=True, margins=True)*100


pd.crosstab(walmart_raw['Occupation'], walmart_raw['Gender'], normalize='index', margins=True)*100


pd.crosstab(walmart_raw['Occupation'], walmart_raw['Gender'], normalize='columns', margins=True)*100


sns.heatmap(pd.crosstab(walmart_raw['Occupation'], walmart_raw['Gender'], normalize='all'),annot=True)
plt.title('Probabbility of Gender ∩ Occupation', fontsize=12)

Text(0.5, 1.0, 'Probabbility of Gender ∩ Occupation')


plt.figure(figsize=(15,10)).suptitle("Walmart Occupation Dashboard",fontsize=14)

plt.subplot(2, 2, 1)
plt.pie(walmart_raw['Occupation'].value_counts().values,labels = walmart_raw['Occupation'].value_counts().index,radius = 1.3,autopct = '%1.2f%%') # type: ignore

plt.subplot(2, 2, 3)
sns.boxplot(walmart_raw, y="Occupation", x='Gender')
plt.ylabel('Occupation Level', fontsize=12)

plt.subplot(3, 2, 2)
sns.histplot(walmart_raw, x='Occupation', binwidth=1, kde=True, hue="Gender")
plt.title('No of Gender Purchase vs Occupation Level', fontsize=12)
plt.ylabel('Count', fontsize=12)
plt.xlabel('', fontsize=11)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)

plt.subplot(3, 2, 4)
sns.countplot(walmart_raw, x='Occupation')
plt.ylabel('Count', fontsize=12)
plt.xlabel('', fontsize=11)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)

plt.subplot(3, 2, 6)
sns.countplot(walmart_raw, x='Occupation', hue='Gender')
plt.ylabel('Count', fontsize=12)
plt.xlabel('Occupation Level', fontsize=11)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)
plt.legend(borderaxespad=1, ncol=1)

plt.show()


(pd.crosstab(walmart_raw['Occupation'], walmart_raw['Gender'], normalize=True)*100).plot(kind='bar')
plt.title('Probability of Gender & Occupation', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=12)
plt.legend(borderaxespad=2, ncol=1)
plt.show()


(pd.crosstab(walmart_raw['Occupation'], walmart_raw['Gender'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of Gender for given Occupation', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=12)
plt.legend(borderaxespad=1, ncol=1)
plt.show()


(pd.crosstab(walmart_raw['Gender'], walmart_raw['Occupation'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of Occupation for given Gender', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=12)
plt.legend(bbox_to_anchor=(1.02, 1), loc='upper center', borderaxespad=5, ncol=2)

plt.show()


pd.crosstab(walmart_raw['Occupation'], walmart_raw['Gender'], normalize="index").plot(kind='pie', subplots=True, figsize=(15,5), labeldistance=None, fontsize=10, legend=None, autopct = '%1.2f%%')
plt.legend(borderaxespad=-1, ncol=7)
plt.show()
pd.crosstab(walmart_raw['Gender'], walmart_raw['Occupation'], normalize="index").plot(kind='pie', subplots=True, figsize=(15,5), labeldistance=None, fontsize=10, legend=None, autopct = '%1.2f%%')
plt.legend(borderaxespad=-1, ncol=3)
plt.show()


walmart_raw['Product_ID'].unique()

array(['P00069042', 'P00248942', 'P00087842', ..., 'P00370293',
       'P00371644', 'P00370853'], dtype=object)


walmart_raw['Product_ID'].value_counts()

P00265242    1880
P00025442    1615
P00110742    1612
P00112142    1562
P00057642    1470
             ... 
P00314842       1
P00298842       1
P00231642       1
P00204442       1
P00066342       1
Name: Product_ID, Length: 3631, dtype: int64


walmart_raw['Product_ID'].describe()

count        550068
unique         3631
top       P00265242
freq           1880
Name: Product_ID, dtype: object


walmart_raw['Product_ID'].mode()[0]

'P00265242'


walmart_raw['Product_ID'].value_counts(normalize=True)*100

P00265242    0.341776
P00025442    0.293600
P00110742    0.293055
P00112142    0.283965
P00057642    0.267240
               ...   
P00314842    0.000182
P00298842    0.000182
P00231642    0.000182
P00204442    0.000182
P00066342    0.000182
Name: Product_ID, Length: 3631, dtype: float64


walmart_raw['Product_Category'].unique()

array([ 3,  1, 12,  8,  5,  4,  2,  6, 14, 11, 13, 15,  7, 16, 18, 10, 17,
        9, 20, 19], dtype=int64)


walmart_raw['Product_Category'].value_counts()

5     150933
1     140378
8     113925
11     24287
2      23864
6      20466
3      20213
4      11753
16      9828
15      6290
13      5549
10      5125
12      3947
7       3721
18      3125
20      2550
19      1603
14      1523
17       578
9        410
Name: Product_Category, dtype: int64


walmart_raw['Product_Category'].value_counts(normalize=True)*100

5     27.438971
1     25.520118
8     20.711076
11     4.415272
2      4.338373
6      3.720631
3      3.674637
4      2.136645
16     1.786688
15     1.143495
13     1.008784
10     0.931703
12     0.717548
7      0.676462
18     0.568112
20     0.463579
19     0.291419
14     0.276875
17     0.105078
9      0.074536
Name: Product_Category, dtype: float64


walmart_raw['Product_Category'].describe()

count    550068.000000
mean          5.404270
std           3.936211
min           1.000000
25%           1.000000
50%           5.000000
75%           8.000000
max          20.000000
Name: Product_Category, dtype: float64


walmart_raw['Product_Category'].mean()

5.404270017525106


walmart_raw['Product_Category'].median()

5.0


walmart_raw['Product_Category'].mode()[0]

5


walmart_raw.groupby('Gender')["Product_Category"].describe()


check_outlier(walmart_raw, 'Product_Category')['upper']

{'list': 545915    20
 545916    20
 545917    20
 545918    20
 545919    20
           ..
 550063    20
 550064    20
 550065    20
 550066    20
 550067    20
 Name: Product_Category, Length: 4153, dtype: int64,
 'length': 4153}


check_outlier(walmart_raw, 'Product_Category')['lower']

{'list': Series([], Name: Product_Category, dtype: int64), 'length': 0}


pd.crosstab(walmart_raw['Product_Category'], walmart_raw['Gender'], normalize=True, margins=True)*100


pd.crosstab(walmart_raw['Product_Category'], walmart_raw['Gender'], normalize='index', margins=True)*100


pd.crosstab(walmart_raw['Product_Category'], walmart_raw['Gender'], normalize='columns', margins=True)*100


sns.heatmap(pd.crosstab(walmart_raw['Product_Category'], walmart_raw['Gender'], normalize='all'),annot=True)
plt.title('Probabbility of Gender ∩ Product Category', fontsize=12)

Text(0.5, 1.0, 'Probabbility of Gender ∩ Product Category')


plt.figure(figsize=(15,10)).suptitle("Walmart Product Category Dashboard",fontsize=14)

plt.subplot(2, 2, 1)
plt.pie(walmart_raw['Product_Category'].value_counts().values,labels = walmart_raw['Product_Category'].value_counts().index,radius = 1.3,autopct = '%1.2f%%') # type: ignore

plt.subplot(2, 2, 3)
sns.boxplot(walmart_raw, y="Product_Category", x='Gender')
plt.ylabel('Product Category Level', fontsize=12)

plt.subplot(3, 2, 2)
sns.histplot(walmart_raw, x='Product_Category', binwidth=1, kde=True, hue="Gender")
plt.title('No of Gender Purchase vs Product Category Level', fontsize=12)
plt.ylabel('Count', fontsize=12)
plt.xlabel('', fontsize=11)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)

plt.subplot(3, 2, 4)
sns.countplot(walmart_raw, x='Product_Category')
plt.ylabel('Count', fontsize=12)
plt.xlabel('', fontsize=11)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)

plt.subplot(3, 2, 6)
sns.countplot(walmart_raw, x='Product_Category', hue='Gender')
plt.ylabel('Count', fontsize=12)
plt.xlabel('Product Category Level', fontsize=11)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)
plt.legend(borderaxespad=1, ncol=1)

plt.show()


(pd.crosstab(walmart_raw['Product_Category'], walmart_raw['Gender'], normalize=True)*100).plot(kind='bar')
plt.title('Probability of Gender & Product Category', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=12)
plt.legend(borderaxespad=2, ncol=1)
plt.show()


(pd.crosstab(walmart_raw['Product_Category'], walmart_raw['Gender'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of Gender for given Gender Category', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=12)
plt.legend(borderaxespad=1, ncol=1)
plt.show()


(pd.crosstab(walmart_raw['Gender'], walmart_raw['Product_Category'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of Gender Category for given Gender', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=12)
plt.legend(bbox_to_anchor=(1.02, 1), loc='upper center', borderaxespad=5, ncol=2)

plt.show()


pd.crosstab(walmart_raw['Product_Category'], walmart_raw['Gender'], normalize="index").plot(kind='pie', subplots=True, figsize=(15,5), labeldistance=None, fontsize=10, legend=None, autopct = '%1.2f%%')
plt.legend(borderaxespad=-1, ncol=10)
plt.show()
pd.crosstab(walmart_raw['Gender'], walmart_raw['Product_Category'], normalize="index").plot(kind='pie', subplots=True, figsize=(15,5), labeldistance=None, fontsize=10, legend=None, autopct = '%1.2f%%')
plt.legend(borderaxespad=-1, ncol=3)
plt.show()


walmart_raw['Purchase_group'].unique()

['7.5K-10K', '15K-17.5K', '1K-2.5K', '17.5K-20K', '5K-7.5K', ..., '501-1K', '20K-22.5K', '22.5K-25K', '101-500', '0-100']
Length: 13
Categories (13, object): ['0-100' < '101-500' < '501-1K' < '1K-2.5K' ... '15K-17.5K' < '17.5K-20K' < '20K-22.5K' < '22.5K-25K']


walmart_raw['Purchase_group'].value_counts()

5K-7.5K      133795
7.5K-10K     131900
15K-17.5K     66463
10K-12.5K     59974
2.5K-5K       58079
17.5K-20K     31369
1K-2.5K       25514
12.5K-15K     18953
20K-22.5K     10341
501-1K         7003
101-500        2724
22.5K-25K      2350
0-100          1603
Name: Purchase_group, dtype: int64


walmart_raw['Purchase_group'].value_counts(normalize=True)*100

5K-7.5K      24.323356
7.5K-10K     23.978854
15K-17.5K    12.082688
10K-12.5K    10.903016
2.5K-5K      10.558513
17.5K-20K     5.702749
1K-2.5K       4.638336
12.5K-15K     3.445574
20K-22.5K     1.879949
501-1K        1.273115
101-500       0.495212
22.5K-25K     0.427220
0-100         0.291419
Name: Purchase_group, dtype: float64


walmart_raw['Purchase'].describe()

count    550068.000000
mean       9263.968713
std        5023.065394
min          12.000000
25%        5823.000000
50%        8047.000000
75%       12054.000000
max       23961.000000
Name: Purchase, dtype: float64


walmart_raw['Purchase'].mean()

9263.968712959126


walmart_raw['Purchase'].median()

8047.0


walmart_raw['Purchase'].mode()[0]

7011


walmart_raw.groupby('Gender')["Purchase"].describe()


walmart_raw['Purchase_group'].describe()

count      550068
unique         13
top       5K-7.5K
freq       133795
Name: Purchase_group, dtype: object


walmart_raw['Purchase_group'].mode()[0]

'5K-7.5K'


walmart_raw.groupby('Gender')["Purchase_group"].describe()


check_outlier(walmart_raw, 'Purchase')['upper']

{'list': 343       23603
 375       23792
 652       23233
 736       23595
 1041      23341
           ...  
 544488    23753
 544704    23724
 544743    23529
 545663    23663
 545787    23496
 Name: Purchase, Length: 2677, dtype: int64,
 'length': 2677}


check_outlier(walmart_raw, 'Purchase')['lower']

{'list': Series([], Name: Purchase, dtype: int64), 'length': 0}


pd.crosstab(walmart_raw['Purchase_group'], walmart_raw['Gender'], normalize=True, margins=True)*100


pd.crosstab(walmart_raw['Purchase_group'], walmart_raw['Gender'], normalize='index', margins=True)*100


pd.crosstab(walmart_raw['Purchase_group'], walmart_raw['Gender'], normalize='columns', margins=True)*100


sns.heatmap(pd.crosstab(walmart_raw['Purchase_group'], walmart_raw['Gender'], normalize='all'),annot=True)
plt.title('Probabbility of Gender ∩ Purchase', fontsize=12)

Text(0.5, 1.0, 'Probabbility of Gender ∩ Purchase')


plt.figure(figsize=(15,15)).suptitle("Walmart Purchase Dashboard",fontsize=14)

plt.subplot(2, 3, 1)
plt.pie(walmart_raw['Purchase_group'].value_counts().values,labels = walmart_raw['Purchase_group'].value_counts().index,radius = 1.3,autopct = '%1.2f%%') # type: ignore

plt.subplot(2, 2, 3)
sns.boxplot(walmart_raw, y="Purchase", x='Gender')
plt.ylabel('Purchase Level', fontsize=12)

plt.subplot(3, 2, 2)
sns.histplot(walmart_raw, x='Purchase', binwidth=1000, kde=True, hue="Gender")
plt.title('No of Gender Purchase vs Purchase Level', fontsize=12)
plt.ylabel('Count', fontsize=12)
plt.xlabel('', fontsize=11)
plt.xticks(fontsize=11)
plt.yticks(rotation= 0, fontsize=11)

plt.subplot(3, 2, 4)
sns.countplot(walmart_raw, x='Purchase_group')
plt.ylabel('Count', fontsize=12)
plt.xlabel('', fontsize=11)
plt.xticks(rotation= 30, fontsize=11)
plt.yticks(fontsize=11)

plt.subplot(3, 2, 6)
sns.countplot(walmart_raw, x='Purchase_group', hue='Gender')
plt.ylabel('Count', fontsize=12)
plt.xlabel('Purchase Level', fontsize=11)
plt.xticks(rotation= 30, fontsize=11)
plt.yticks(fontsize=11)
plt.legend(borderaxespad=1, ncol=1)

plt.show()


(pd.crosstab(walmart_raw['Purchase_group'], walmart_raw['Gender'], normalize=True)*100).plot(kind='bar')
plt.title('Probability of Gender & Purchase', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 40, fontsize=12)
plt.yticks(rotation= 0, fontsize=12)
plt.legend(borderaxespad=2, ncol=1)
plt.show()


(pd.crosstab(walmart_raw['Purchase_group'], walmart_raw['Gender'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of Gender for given Purchase', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 40, fontsize=12)
plt.yticks(rotation= 0, fontsize=12)
plt.legend(borderaxespad=1, ncol=1)
plt.show()


(pd.crosstab(walmart_raw['Gender'], walmart_raw['Purchase_group'], normalize="index")*100).plot(kind='bar')
plt.title('Probability of Purchase for given Gender', fontsize=15)
plt.ylabel('Probability in %', fontsize=12)
plt.xlabel('', fontsize=12)
plt.xticks(rotation= 360, fontsize=12)
plt.yticks(rotation= 0, fontsize=12)
plt.legend(bbox_to_anchor=(1.02, 1), loc='upper center', borderaxespad=5, ncol=2)

plt.show()


pd.crosstab(walmart_raw['Purchase_group'], walmart_raw['Gender'], normalize="index").plot(kind='pie', subplots=True, figsize=(15,5), labeldistance=None, fontsize=10, legend=None, autopct = '%1.2f%%')
plt.legend(borderaxespad=-1, ncol=7)
plt.show()
pd.crosstab(walmart_raw['Gender'], walmart_raw['Purchase_group'], normalize="index").plot(kind='pie', subplots=True, figsize=(15,5), labeldistance=None, fontsize=10, legend=None, autopct = '%1.2f%%')
plt.legend(borderaxespad=-1, ncol=3)
plt.show()


plt.figure(figsize=(20,15)).suptitle("Walmart Customer Profile",fontsize=14)

plt.subplot(2, 2, 1)
sns.heatmap(walmart_raw[['gender', 'Marital_Status', 'age', 'Occupation', 'Product_Category', 'Purchase']].corr(), annot=True)
plt.yticks(rotation= 0, fontsize=11)
plt.xticks(rotation= 20, fontsize=11)

plt.subplot(2, 2, 2)
sns.histplot(walmart_raw, x="Purchase", binwidth=1500, kde=True, hue="Gender", element="step")

plt.subplot(4, 3, 7)
sns.countplot(walmart_raw, y='marital_status', hue="Gender")
plt.xlabel("", fontsize=11)
plt.ylabel("Marital Status", fontsize=11)
plt.subplot(4, 3, 8)
sns.countplot(walmart_raw, y='City_Category', hue="Gender")
plt.xlabel("Count", fontsize=11)
plt.subplot(4, 3, 9)
sns.countplot(walmart_raw, y='Stay_In_Current_City_Years', hue="Gender")
plt.xlabel("Count", fontsize=11)

plt.subplot(4, 3, 10)
sns.countplot(walmart_raw, y='Age', hue="Gender")
plt.xlabel("", fontsize=11)
plt.subplot(4, 3, 11)
sns.histplot(walmart_raw, x="Occupation", binwidth=1, kde=True, hue="Gender", element="step")
plt.subplot(4, 3, 12)
sns.histplot(walmart_raw, x="Product_Category", binwidth=1, kde=True, hue="Gender", element="step")

plt.show()


Male = walmart_raw.copy().loc[walmart_raw['Gender']=='Male']


Male.shape[0]

414259


Male.nunique()

User_ID                        4225
Gender                            1
gender                            1
Marital_Status                    2
marital_status                    2
Age                               7
age                               7
City_Category                     3
Stay_In_Current_City_Years        5
Occupation                       21
Product_ID                     3588
Product_Category                 20
Purchase                      17547
Purchase_group                   13
dtype: int64


Male[['marital_status', 'Age', 'City_Category', 'Stay_In_Current_City_Years', 'Product_ID', 'Purchase_group', 'User_ID']].describe()


Male[['age', 'Occupation' , 'Product_Category', 'Purchase']].describe()


Male[['age', 'Purchase']].mean()

age           34.23554
Purchase    9437.52604
dtype: float64


Male[['age', 'Purchase']].median()

age           30.5
Purchase    8098.0
dtype: float64


pd.crosstab([Male.Purchase_group], [Male.Age], normalize=True, margins=True)*100


pd.crosstab([Male.marital_status, Male.City_Category], [Male.Purchase_group], normalize=True, margins=True)*100


pd.crosstab([Male.marital_status, Male.Stay_In_Current_City_Years], [Male.Purchase_group], normalize=True, margins=True)*100


pd.crosstab([Male.marital_status, Male.City_Category], [Male.Age], normalize=True, margins=True)*100


pd.crosstab([Male.marital_status, Male.Stay_In_Current_City_Years], [Male.Age], normalize=True, margins=True)*100


pd.crosstab([Male.Age], [Male.Purchase_group], normalize=True, margins=True)*100


pd.crosstab([Male.Occupation], [Male.Purchase_group], normalize=True, margins=True)*100


plt.figure(figsize=(15,10)).suptitle("Male Outliers",fontsize=14)

plt.subplot(2, 2, 1)
sns.boxplot(Male, y='age')
plt.subplot(2, 2, 2)
sns.boxplot(Male, y='Occupation')

plt.subplot(2, 2, 3)
sns.boxplot(Male, y="Product_Category")
plt.subplot(2, 2, 4)
sns.boxplot(Male, y="Purchase")

plt.show()


plt.figure(figsize=(20,15)).suptitle("Male Customer Profile",fontsize=14)

plt.subplot(2, 2, 1)
sns.heatmap(Male[['Marital_Status', 'age', 'Occupation', 'Product_Category', 'Purchase']].corr(), annot=True)
plt.yticks(rotation= 0, fontsize=11)
plt.xticks(rotation= 20, fontsize=11)

plt.subplot(2, 2, 2)
sns.histplot(Male, x="Purchase", kde=True)

plt.subplot(4, 3, 7)
sns.countplot(Male, y='marital_status')
plt.xlabel("", fontsize=11)
plt.ylabel("Marital Status", fontsize=11)
plt.subplot(4, 3, 8)
sns.countplot(Male, y='City_Category')
plt.xlabel("Count", fontsize=11)
plt.subplot(4, 3, 9)
sns.countplot(Male, y='Stay_In_Current_City_Years')
plt.xlabel("Count", fontsize=11)

plt.subplot(4, 3, 10)
sns.countplot(Male, y='Age')
plt.xlabel("", fontsize=11)
plt.subplot(4, 3, 11)
sns.histplot(Male, x="Occupation", binwidth=1, kde=True, element="step")
plt.subplot(4, 3, 12)
sns.histplot(Male, x="Product_Category", binwidth=1, kde=True, element="step")

plt.show()


Female = walmart_raw.copy().loc[walmart_raw['Gender']=='Female']


Female.shape[0]

135809


Female.nunique()

User_ID                        1666
Gender                            1
gender                            1
Marital_Status                    2
marital_status                    2
Age                               7
age                               7
City_Category                     3
Stay_In_Current_City_Years        5
Occupation                       21
Product_ID                     3367
Product_Category                 20
Purchase                      14704
Purchase_group                   13
dtype: int64


Female[['marital_status', 'Age', 'City_Category', 'Stay_In_Current_City_Years', 'Product_ID', 'Purchase_group', 'User_ID']].describe()


Female[['age', 'Occupation' , 'Product_Category', 'Purchase']].describe()


Female[['age', 'Purchase']].mean()

age           34.302053
Purchase    8734.565765
dtype: float64


Female[['age', 'Purchase']].median()

age           30.5
Purchase    7914.0
dtype: float64


pd.crosstab([Female.Purchase_group], [Female.Age], normalize=True, margins=True)*100


pd.crosstab([Female.marital_status, Female.City_Category], [Female.Purchase_group], normalize=True, margins=True)*100


pd.crosstab([Female.marital_status, Female.Stay_In_Current_City_Years], [Female.Purchase_group], normalize=True, margins=True)*100


pd.crosstab([Female.marital_status, Female.City_Category], [Female.Age], normalize=True, margins=True)*100


pd.crosstab([Female.marital_status, Female.Stay_In_Current_City_Years], [Female.Age], normalize=True, margins=True)*100


pd.crosstab([Female.Age], [Female.Purchase_group], normalize=True, margins=True)*100


pd.crosstab([Female.Occupation], [Female.Purchase_group], normalize=True, margins=True)*100


plt.figure(figsize=(15,10)).suptitle("Female Outliers",fontsize=14)

plt.subplot(2, 2, 1)
sns.boxplot(Female, y='age')
plt.subplot(2, 2, 2)
sns.boxplot(Female, y='Occupation')

plt.subplot(2, 2, 3)
sns.boxplot(Female, y="Product_Category")
plt.subplot(2, 2, 4)
sns.boxplot(Female, y="Purchase")

plt.show()


plt.figure(figsize=(20,15)).suptitle("Female Customer Profile",fontsize=14)

plt.subplot(2, 2, 1)
sns.heatmap(Female[['Marital_Status', 'age', 'Occupation', 'Product_Category', 'Purchase']].corr(), annot=True)
plt.yticks(rotation= 0, fontsize=11)
plt.xticks(rotation= 20, fontsize=11)

plt.subplot(2, 2, 2)
sns.histplot(Female, x="Purchase", kde=True)
# sns.histplot(Female, x="Purchase", binwidth=300, kde=True, element="step")

plt.subplot(4, 3, 7)
sns.countplot(Female, y='marital_status')
plt.xlabel("", fontsize=11)
plt.ylabel("Marital Status", fontsize=11)
plt.subplot(4, 3, 8)
sns.countplot(Female, y='City_Category')
plt.xlabel("Count", fontsize=11)
plt.subplot(4, 3, 9)
sns.countplot(Female, y='Stay_In_Current_City_Years')
plt.xlabel("Count", fontsize=11)

plt.subplot(4, 3, 10)
sns.countplot(Female, y='Age')
plt.xlabel("", fontsize=11)
plt.subplot(4, 3, 11)
sns.histplot(Female, x="Occupation", binwidth=1, kde=True, element="step")
plt.subplot(4, 3, 12)
sns.histplot(Female, x="Product_Category", binwidth=1, kde=True, element="step")

plt.show()


def filter_col_as_array(df, col, match, result_col, inverse=False):
    if inverse:
        return df[df[col]!=match][result_col]
    else :
        return df[df[col]==match][result_col]


class CLT_Interval:
    def __init__(self, mean, std_error, z_critical, margin_of_error, confidance_interval_lower, confidance_interval_upper, sample_df, significance_level, samples_size):
        self.mean = mean
        self.std_error = std_error
        self.z_critical = z_critical
        self.margin_of_error = margin_of_error
        self.confidance_interval_lower = confidance_interval_lower
        self.confidance_interval_upper = confidance_interval_upper
        self.sample_df = sample_df
        self.significance_level = significance_level
        self.samples_size = samples_size


def bootstraping(df, samples, cl, plot=True):
    bootstrap_sample_means = []

    for _ in range(samples):
        bootstrap_sample = df.sample(n=len(df), replace=True)
        bootstrap_sample_mean = bootstrap_sample.mean()
        bootstrap_sample_means.append(bootstrap_sample_mean)
    
    bootstrap_mean = np.mean(bootstrap_sample_means)
    bootstrap_std_error = np.std(bootstrap_sample_means)

    z_critical = norm.ppf((1 + cl) / 2)

    # Calculate the margin of error
    margin_of_error = z_critical * bootstrap_std_error

    # Calculate the confidence interval
    confidence_interval_lower = bootstrap_mean - margin_of_error
    confidence_interval_upper = bootstrap_mean + margin_of_error

    if(plot):
        plt.figure(figsize=(8, 4))
        sns.histplot(data=bootstrap_sample_means, bins=20, color='#66a3ff', label='Sample Mean', kde=True)
        plt.axvline(bootstrap_mean, color='#ff0066', linestyle='dashed', linewidth=2, label='Mean')
        plt.axvline(confidence_interval_lower, color='#ff8533', linestyle='dotted', linewidth=2, label='CI Lower')
        plt.axvline(confidence_interval_upper, color='#ff8533', linestyle='dotted', linewidth=2, label='CI Upper')
        plt.annotate(f'Mean: {bootstrap_mean:.2f}', rotation='vertical', xy=(bootstrap_mean, 0), xytext=(-20, 30), textcoords='offset points', color='blacK', fontsize=15)
        plt.annotate(f'{confidence_interval_lower:.2f}', rotation='vertical', xy=(confidence_interval_lower, 0), xytext=(-20, 80), textcoords='offset points', color='blacK', fontsize=14)
        plt.annotate(f'{confidence_interval_upper:.2f}', rotation='vertical', xy=(confidence_interval_upper, 0), xytext=(10, 80), textcoords='offset points', color='blacK', fontsize=14)
        plt.legend()

    return CLT_Interval(bootstrap_mean, bootstrap_std_error, z_critical, margin_of_error, confidence_interval_lower, confidence_interval_upper, bootstrap_sample_means, cl, samples)


df = walmart_raw['Purchase']
samples=500
confidence_level = 0.9
clt_interval = bootstraping(df, samples, cl=confidence_level, plot=True)

print("Bootstrap Mean:", clt_interval.mean)
print("Bootstrap Standard Error:", clt_interval.std_error)
print("Z-Critical Value:", clt_interval.z_critical)
print("Margin of Error:", clt_interval.margin_of_error)
print("Bootstrap Confidence Interval:", (clt_interval.confidance_interval_lower, clt_interval.confidance_interval_upper))

Bootstrap Mean: 9263.472707727045
Bootstrap Standard Error: 6.804071836180344
Z-Critical Value: 1.6448536269514722
Margin of Error: 11.191702237779602
Bootstrap Confidence Interval: (9252.281005489265, 9274.664409964826)


average_spending_by_gender = walmart_raw.groupby('Gender')['Purchase'].mean().reset_index()
average_spending_by_gender


sns.barplot(x=average_spending_by_gender.Gender, y=average_spending_by_gender.Purchase)

<Axes: xlabel='Gender', ylabel='Purchase'>


#@ Ho -> Man Spend more than Women on Black Friday
## Ha -> Women Spend more than Man on Black Friday

# % We will use 500 Sample
samples=500


male_df = filter_col_as_array(df=walmart_raw, col='Gender', match='Male', result_col='Purchase')
female_df = filter_col_as_array(df=walmart_raw, col='Gender', match='Female', result_col='Purchase')


t_statistic, p_value = ttest_ind(female_df, male_df, alternative="greater")
t_statistic, p_value

(-44.837957934353966, 1.0)


confidence_level = 0.90
sigma = 1-confidence_level #$ Significance Level


if p_value < sigma:
    print("Women Spend more than Man on Black Friday")
else:
    print("Man Spend more than Women on Black Friday")

Man Spend more than Women on Black Friday


male_clt_interval = bootstraping(male_df, samples, cl=confidence_level, plot=False)
female_clt_interval = bootstraping(female_df, samples, cl=confidence_level, plot=False)
# mean, std_error, z_critical, margin_of_error, confidance_interval_lower, confidance_interval_upper, sample_df, significance_level, samples_size

plt.figure(figsize=(20,8)).suptitle("Walmart Male vs Female Purchase at 90% Confidance Interval",fontsize=14)

plt.subplot(1, 2, 1)
sns.histplot(data=female_clt_interval.sample_df, bins=20, color='#ff66ff', label='Female Spending', kde=True)
plt.axvline(female_clt_interval.mean, color='#0099ff', linestyle='dashed', linewidth=2, label='Female Mean')
plt.axvline(female_clt_interval.confidance_interval_lower, color='#8000ff', linestyle='dotted', linewidth=2, label='Female CI Lower')
plt.axvline(female_clt_interval.confidance_interval_upper, color='#8000ff', linestyle='dotted', linewidth=2, label='Female CI Upper')
plt.annotate(f'Mean: {female_clt_interval.mean:.2f}', rotation='vertical', xy=(female_clt_interval.mean, 0), xytext=(-20, 60), textcoords='offset points', color='blacK', fontsize=15)
plt.annotate(f'{female_clt_interval.confidance_interval_lower:.2f}', rotation='vertical', xy=(female_clt_interval.confidance_interval_lower, 0), xytext=(-20, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.annotate(f'{female_clt_interval.confidance_interval_upper:.2f}', rotation='vertical', xy=(female_clt_interval.confidance_interval_upper, 0), xytext=(10, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.xlabel('Spending')
plt.ylabel('Frequency')
plt.title('Female\'s Purchase')
plt.legend()

plt.subplot(1, 2, 2)
sns.histplot(data=male_clt_interval.sample_df, bins=20, color='#66a3ff', label='Male Spending', kde=True)
plt.axvline(male_clt_interval.confidance_interval_lower, color='#ff8533', linestyle='dotted', linewidth=2, label='Male CI Lower')
plt.axvline(male_clt_interval.confidance_interval_upper, color='#ff8533', linestyle='dotted', linewidth=2, label='Male CI Upper')
plt.axvline(male_clt_interval.mean, color='#ff0066', linestyle='dashed', linewidth=2, label='Male Mean')
plt.annotate(f'Mean: {male_clt_interval.mean:.2f}', rotation='vertical', xy=(male_clt_interval.mean, 0), xytext=(-20, 60), textcoords='offset points', color='blacK', fontsize=15)
plt.annotate(f'{male_clt_interval.confidance_interval_lower:.2f}', rotation='vertical', xy=(male_clt_interval.confidance_interval_lower, 0), xytext=(-20, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.annotate(f'{male_clt_interval.confidance_interval_upper:.2f}', rotation='vertical', xy=(male_clt_interval.confidance_interval_upper, 0), xytext=(10, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.xlabel('Spending')
plt.ylabel('Frequency')
plt.title('Male\'s Purchase')
plt.legend()

plt.show()


confidence_level = 0.95
sigma = 1-confidence_level #$ Significance Level


if p_value < sigma:
    print("Women Spend more than Man on Black Friday")
else:
    print("Man Spend more than Women on Black Friday")

Man Spend more than Women on Black Friday


male_clt_interval = bootstraping(male_df, samples, cl=confidence_level, plot=False)
female_clt_interval = bootstraping(female_df, samples, cl=confidence_level, plot=False)
# mean, std_error, z_critical, margin_of_error, confidance_interval_lower, confidance_interval_upper, sample_df, significance_level, samples_size

plt.figure(figsize=(20,8)).suptitle("Walmart Male vs Female Purchase at 95% Confidance Interval",fontsize=14)

plt.subplot(1, 2, 1)
sns.histplot(data=female_clt_interval.sample_df, bins=20, color='#ff66ff', label='Female Spending', kde=True)
plt.axvline(female_clt_interval.mean, color='#0099ff', linestyle='dashed', linewidth=2, label='Female Mean')
plt.axvline(female_clt_interval.confidance_interval_lower, color='#8000ff', linestyle='dotted', linewidth=2, label='Female CI Lower')
plt.axvline(female_clt_interval.confidance_interval_upper, color='#8000ff', linestyle='dotted', linewidth=2, label='Female CI Upper')
plt.annotate(f'Mean: {female_clt_interval.mean:.2f}', rotation='vertical', xy=(female_clt_interval.mean, 0), xytext=(-20, 60), textcoords='offset points', color='blacK', fontsize=15)
plt.annotate(f'{female_clt_interval.confidance_interval_lower:.2f}', rotation='vertical', xy=(female_clt_interval.confidance_interval_lower, 0), xytext=(-20, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.annotate(f'{female_clt_interval.confidance_interval_upper:.2f}', rotation='vertical', xy=(female_clt_interval.confidance_interval_upper, 0), xytext=(10, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.xlabel('Spending')
plt.ylabel('Frequency')
plt.title('Female\'s Purchase')
plt.legend()

plt.subplot(1, 2, 2)
sns.histplot(data=male_clt_interval.sample_df, bins=20, color='#66a3ff', label='Male Spending', kde=True)
plt.axvline(male_clt_interval.confidance_interval_lower, color='#ff8533', linestyle='dotted', linewidth=2, label='Male CI Lower')
plt.axvline(male_clt_interval.confidance_interval_upper, color='#ff8533', linestyle='dotted', linewidth=2, label='Male CI Upper')
plt.axvline(male_clt_interval.mean, color='#ff0066', linestyle='dashed', linewidth=2, label='Male Mean')
plt.annotate(f'Mean: {male_clt_interval.mean:.2f}', rotation='vertical', xy=(male_clt_interval.mean, 0), xytext=(-20, 60), textcoords='offset points', color='blacK', fontsize=15)
plt.annotate(f'{male_clt_interval.confidance_interval_lower:.2f}', rotation='vertical', xy=(male_clt_interval.confidance_interval_lower, 0), xytext=(-20, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.annotate(f'{male_clt_interval.confidance_interval_upper:.2f}', rotation='vertical', xy=(male_clt_interval.confidance_interval_upper, 0), xytext=(10, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.xlabel('Spending')
plt.ylabel('Frequency')
plt.title('Male\'s Purchase')
plt.legend()

plt.show()


confidence_level = 0.99
sigma = 1-confidence_level #$ Significance Level


if p_value < sigma:
    print("Women Spend more than Man on Black Friday")
else:
    print("Man Spend more than Women on Black Friday")

Man Spend more than Women on Black Friday


male_clt_interval = bootstraping(male_df, samples, cl=confidence_level, plot=False)
female_clt_interval = bootstraping(female_df, samples, cl=confidence_level, plot=False)
# mean, std_error, z_critical, margin_of_error, confidance_interval_lower, confidance_interval_upper, sample_df, significance_level, samples_size

plt.figure(figsize=(20,8)).suptitle("Walmart Male vs Female Purchase at 99% Confidance Interval",fontsize=14)

plt.subplot(1, 2, 1)
sns.histplot(data=female_clt_interval.sample_df, bins=20, color='#ff66ff', label='Female Spending', kde=True)
plt.axvline(female_clt_interval.mean, color='#0099ff', linestyle='dashed', linewidth=2, label='Female Mean')
plt.axvline(female_clt_interval.confidance_interval_lower, color='#8000ff', linestyle='dotted', linewidth=2, label='Female CI Lower')
plt.axvline(female_clt_interval.confidance_interval_upper, color='#8000ff', linestyle='dotted', linewidth=2, label='Female CI Upper')
plt.annotate(f'Mean: {female_clt_interval.mean:.2f}', rotation='vertical', xy=(female_clt_interval.mean, 0), xytext=(-20, 60), textcoords='offset points', color='blacK', fontsize=15)
plt.annotate(f'{female_clt_interval.confidance_interval_lower:.2f}', rotation='vertical', xy=(female_clt_interval.confidance_interval_lower, 0), xytext=(-20, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.annotate(f'{female_clt_interval.confidance_interval_upper:.2f}', rotation='vertical', xy=(female_clt_interval.confidance_interval_upper, 0), xytext=(10, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.xlabel('Spending')
plt.ylabel('Frequency')
plt.title('Female\'s Purchase')
plt.legend()

plt.subplot(1, 2, 2)
sns.histplot(data=male_clt_interval.sample_df, bins=20, color='#66a3ff', label='Male Spending', kde=True)
plt.axvline(male_clt_interval.confidance_interval_lower, color='#ff8533', linestyle='dotted', linewidth=2, label='Male CI Lower')
plt.axvline(male_clt_interval.confidance_interval_upper, color='#ff8533', linestyle='dotted', linewidth=2, label='Male CI Upper')
plt.axvline(male_clt_interval.mean, color='#ff0066', linestyle='dashed', linewidth=2, label='Male Mean')
plt.annotate(f'Mean: {male_clt_interval.mean:.2f}', rotation='vertical', xy=(male_clt_interval.mean, 0), xytext=(-20, 60), textcoords='offset points', color='blacK', fontsize=15)
plt.annotate(f'{male_clt_interval.confidance_interval_lower:.2f}', rotation='vertical', xy=(male_clt_interval.confidance_interval_lower, 0), xytext=(-20, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.annotate(f'{male_clt_interval.confidance_interval_upper:.2f}', rotation='vertical', xy=(male_clt_interval.confidance_interval_upper, 0), xytext=(10, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.xlabel('Spending')
plt.ylabel('Frequency')
plt.title('Male\'s Purchase')
plt.legend()

plt.show()


average_spending_by_marital_status = walmart_raw.groupby('marital_status')['Purchase'].mean().reset_index()
average_spending_by_marital_status


sns.barplot(x=average_spending_by_marital_status.marital_status, y=average_spending_by_marital_status.Purchase)

<Axes: xlabel='marital_status', ylabel='Purchase'>


#@ Ho -> Single & Partnered peoples are Spend significantly equal on Black Friday
## Ha -> Single & Partnered peoples are Spend significantly different on Black Friday

# % We will use 500 Sample
samples=500


single_df = filter_col_as_array(df=walmart_raw, col='marital_status', match='Single', result_col='Purchase')
partnered_df = filter_col_as_array(df=walmart_raw, col='marital_status', match='Partnered', result_col='Purchase')


t_statistic, p_value = ttest_ind(single_df, partnered_df)
t_statistic, p_value

(0.3436698055440526, 0.7310947525758316)


confidence_level = 0.90
sigma = 1-confidence_level #$ Significance Level


if p_value < sigma:
    print("Single & Partnered peoples are Spend significantly different on Black Friday")
else:
    print("Single & Partnered peoples are Spend significantly equal on Black Friday")

Single & Partnered peoples are Spend significantly equal on Black Friday


single_clt_interval = bootstraping(single_df, samples, cl=confidence_level, plot=False)
partnered_clt_interval = bootstraping(partnered_df, samples, cl=confidence_level, plot=False)
# mean, std_error, z_critical, margin_of_error, confidance_interval_lower, confidance_interval_upper, sample_df, significance_level, samples_size

plt.figure(figsize=(20,8)).suptitle("Walmart Single vs Partnered Purchase at 90% Confidance Interval",fontsize=14)

plt.subplot(1, 2, 1)
sns.histplot(data=partnered_clt_interval.sample_df, bins=20, color='#ff66ff', label='partnered Spending', kde=True)
plt.axvline(partnered_clt_interval.mean, color='#0099ff', linestyle='dashed', linewidth=2, label='Partnered Mean')
plt.axvline(partnered_clt_interval.confidance_interval_lower, color='#8000ff', linestyle='dotted', linewidth=2, label='Partnered CI Lower')
plt.axvline(partnered_clt_interval.confidance_interval_upper, color='#8000ff', linestyle='dotted', linewidth=2, label='Partnered CI Upper')
plt.annotate(f'Mean: {partnered_clt_interval.mean:.2f}', rotation='vertical', xy=(partnered_clt_interval.mean, 0), xytext=(-20, 60), textcoords='offset points', color='blacK', fontsize=15)
plt.annotate(f'{partnered_clt_interval.confidance_interval_lower:.2f}', rotation='vertical', xy=(partnered_clt_interval.confidance_interval_lower, 0), xytext=(-20, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.annotate(f'{partnered_clt_interval.confidance_interval_upper:.2f}', rotation='vertical', xy=(partnered_clt_interval.confidance_interval_upper, 0), xytext=(10, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.xlabel('Spending')
plt.ylabel('Frequency')
plt.title('Partnered people\'s Purchase')
plt.legend()

plt.subplot(1, 2, 2)
sns.histplot(data=single_clt_interval.sample_df, bins=20, color='#66a3ff', label='Single Spending', kde=True)
plt.axvline(single_clt_interval.confidance_interval_lower, color='#ff8533', linestyle='dotted', linewidth=2, label='Single CI Lower')
plt.axvline(single_clt_interval.confidance_interval_upper, color='#ff8533', linestyle='dotted', linewidth=2, label='Single CI Upper')
plt.axvline(single_clt_interval.mean, color='#ff0066', linestyle='dashed', linewidth=2, label='single Mean')
plt.annotate(f'Mean: {single_clt_interval.mean:.2f}', rotation='vertical', xy=(single_clt_interval.mean, 0), xytext=(-20, 60), textcoords='offset points', color='blacK', fontsize=15)
plt.annotate(f'{single_clt_interval.confidance_interval_lower:.2f}', rotation='vertical', xy=(single_clt_interval.confidance_interval_lower, 0), xytext=(-20, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.annotate(f'{single_clt_interval.confidance_interval_upper:.2f}', rotation='vertical', xy=(single_clt_interval.confidance_interval_upper, 0), xytext=(10, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.xlabel('Spending')
plt.ylabel('Frequency')
plt.title('Single people\'s Purchase')
plt.legend()

plt.show()


confidence_level = 0.95
sigma = 1-confidence_level #$ Significance Level


if p_value < sigma:
    print("Single & Partnered peoples are Spend significantly different on Black Friday")
else:
    print("Single & Partnered peoples are Spend significantly equal on Black Friday")

Single & Partnered peoples are Spend significantly equal on Black Friday


single_clt_interval = bootstraping(single_df, samples, cl=confidence_level, plot=False)
partnered_clt_interval = bootstraping(partnered_df, samples, cl=confidence_level, plot=False)
# mean, std_error, z_critical, margin_of_error, confidance_interval_lower, confidance_interval_upper, sample_df, significance_level, samples_size

plt.figure(figsize=(20,8)).suptitle("Walmart Single vs Partnered Purchase at 95% Confidance Interval",fontsize=14)

plt.subplot(1, 2, 1)
sns.histplot(data=partnered_clt_interval.sample_df, bins=20, color='#ff66ff', label='partnered Spending', kde=True)
plt.axvline(partnered_clt_interval.mean, color='#0099ff', linestyle='dashed', linewidth=2, label='Partnered Mean')
plt.axvline(partnered_clt_interval.confidance_interval_lower, color='#8000ff', linestyle='dotted', linewidth=2, label='Partnered CI Lower')
plt.axvline(partnered_clt_interval.confidance_interval_upper, color='#8000ff', linestyle='dotted', linewidth=2, label='Partnered CI Upper')
plt.annotate(f'Mean: {partnered_clt_interval.mean:.2f}', rotation='vertical', xy=(partnered_clt_interval.mean, 0), xytext=(-20, 60), textcoords='offset points', color='blacK', fontsize=15)
plt.annotate(f'{partnered_clt_interval.confidance_interval_lower:.2f}', rotation='vertical', xy=(partnered_clt_interval.confidance_interval_lower, 0), xytext=(-20, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.annotate(f'{partnered_clt_interval.confidance_interval_upper:.2f}', rotation='vertical', xy=(partnered_clt_interval.confidance_interval_upper, 0), xytext=(10, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.xlabel('Spending')
plt.ylabel('Frequency')
plt.title('Partnered people\'s Purchase')
plt.legend()

plt.subplot(1, 2, 2)
sns.histplot(data=single_clt_interval.sample_df, bins=20, color='#66a3ff', label='Single Spending', kde=True)
plt.axvline(single_clt_interval.confidance_interval_lower, color='#ff8533', linestyle='dotted', linewidth=2, label='Single CI Lower')
plt.axvline(single_clt_interval.confidance_interval_upper, color='#ff8533', linestyle='dotted', linewidth=2, label='Single CI Upper')
plt.axvline(single_clt_interval.mean, color='#ff0066', linestyle='dashed', linewidth=2, label='single Mean')
plt.annotate(f'Mean: {single_clt_interval.mean:.2f}', rotation='vertical', xy=(single_clt_interval.mean, 0), xytext=(-20, 60), textcoords='offset points', color='blacK', fontsize=15)
plt.annotate(f'{single_clt_interval.confidance_interval_lower:.2f}', rotation='vertical', xy=(single_clt_interval.confidance_interval_lower, 0), xytext=(-20, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.annotate(f'{single_clt_interval.confidance_interval_upper:.2f}', rotation='vertical', xy=(single_clt_interval.confidance_interval_upper, 0), xytext=(10, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.xlabel('Spending')
plt.ylabel('Frequency')
plt.title('Single people\'s Purchase')
plt.legend()

plt.show()


confidence_level = 0.99
sigma = 1-confidence_level #$ Significance Level


if p_value < sigma:
    print("Single & Partnered peoples are Spend significantly different on Black Friday")
else:
    print("Single & Partnered peoples are Spend significantly equal on Black Friday")

Single & Partnered peoples are Spend significantly equal on Black Friday


single_clt_interval = bootstraping(single_df, samples, cl=confidence_level, plot=False)
partnered_clt_interval = bootstraping(partnered_df, samples, cl=confidence_level, plot=False)
# mean, std_error, z_critical, margin_of_error, confidance_interval_lower, confidance_interval_upper, sample_df, significance_level, samples_size

plt.figure(figsize=(20,8)).suptitle("Walmart Single vs Partnered Purchase at 99% Confidance Interval",fontsize=14)

plt.subplot(1, 2, 1)
sns.histplot(data=partnered_clt_interval.sample_df, bins=20, color='#ff66ff', label='partnered Spending', kde=True)
plt.axvline(partnered_clt_interval.mean, color='#0099ff', linestyle='dashed', linewidth=2, label='Partnered Mean')
plt.axvline(partnered_clt_interval.confidance_interval_lower, color='#8000ff', linestyle='dotted', linewidth=2, label='Partnered CI Lower')
plt.axvline(partnered_clt_interval.confidance_interval_upper, color='#8000ff', linestyle='dotted', linewidth=2, label='Partnered CI Upper')
plt.annotate(f'Mean: {partnered_clt_interval.mean:.2f}', rotation='vertical', xy=(partnered_clt_interval.mean, 0), xytext=(-20, 60), textcoords='offset points', color='blacK', fontsize=15)
plt.annotate(f'{partnered_clt_interval.confidance_interval_lower:.2f}', rotation='vertical', xy=(partnered_clt_interval.confidance_interval_lower, 0), xytext=(-20, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.annotate(f'{partnered_clt_interval.confidance_interval_upper:.2f}', rotation='vertical', xy=(partnered_clt_interval.confidance_interval_upper, 0), xytext=(10, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.xlabel('Spending')
plt.ylabel('Frequency')
plt.title('Partnered people\'s Purchase')
plt.legend()

plt.subplot(1, 2, 2)
sns.histplot(data=single_clt_interval.sample_df, bins=20, color='#66a3ff', label='Single Spending', kde=True)
plt.axvline(single_clt_interval.confidance_interval_lower, color='#ff8533', linestyle='dotted', linewidth=2, label='Single CI Lower')
plt.axvline(single_clt_interval.confidance_interval_upper, color='#ff8533', linestyle='dotted', linewidth=2, label='Single CI Upper')
plt.axvline(single_clt_interval.mean, color='#ff0066', linestyle='dashed', linewidth=2, label='single Mean')
plt.annotate(f'Mean: {single_clt_interval.mean:.2f}', rotation='vertical', xy=(single_clt_interval.mean, 0), xytext=(-20, 60), textcoords='offset points', color='blacK', fontsize=15)
plt.annotate(f'{single_clt_interval.confidance_interval_lower:.2f}', rotation='vertical', xy=(single_clt_interval.confidance_interval_lower, 0), xytext=(-20, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.annotate(f'{single_clt_interval.confidance_interval_upper:.2f}', rotation='vertical', xy=(single_clt_interval.confidance_interval_upper, 0), xytext=(10, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.xlabel('Spending')
plt.ylabel('Frequency')
plt.title('Single people\'s Purchase')
plt.legend()

plt.show()


average_spending_by_age = walmart_raw.groupby('Age')['Purchase'].mean().reset_index().sort_values('Purchase', ascending=False)
average_spending_by_age


sns.barplot(x=average_spending_by_age.Age, y=average_spending_by_age.Purchase)

<Axes: xlabel='Age', ylabel='Purchase'>


#@ Ho -> Mean Purchase for All Age group are Same.
## Ha -> Differance exists in Mean Purchase for All Age group.

# % We will use 500 Sample
samples=500


df_0_17 = filter_col_as_array(df=walmart_raw, col='Age', match='0-17', result_col='Purchase')
df_18_25 = filter_col_as_array(df=walmart_raw, col='Age', match='18-25', result_col='Purchase')
df_26_35 = filter_col_as_array(df=walmart_raw, col='Age', match='26-35', result_col='Purchase')
df_36_45 = filter_col_as_array(df=walmart_raw, col='Age', match='36-45', result_col='Purchase')
df_46_50 = filter_col_as_array(df=walmart_raw, col='Age', match='46-50', result_col='Purchase')
df_51_55 = filter_col_as_array(df=walmart_raw, col='Age', match='51-55', result_col='Purchase')
df_55 = filter_col_as_array(df=walmart_raw, col='Age', match='55+', result_col='Purchase')


t_statistic, p_value = f_oneway(df_0_17, df_18_25, df_26_35, df_36_45, df_46_50, df_51_55, df_55)
t_statistic, p_value

(40.57579909450407, 1.053563939251671e-49)


confidence_level = 0.90
sigma = 1-confidence_level #$ Significance Level


if p_value < sigma:
    print("Differance exists in Mean Purchase for All Age group.")
else:
    print("Mean Purchase for All Age group are Same.")

Differance exists in Mean Purchase for All Age group.


df_0_17_clt_interval = bootstraping(df_0_17, samples, cl=confidence_level, plot=False)
df_51_55_clt_interval = bootstraping(df_51_55, samples, cl=confidence_level, plot=False)
# mean, std_error, z_critical, margin_of_error, confidance_interval_lower, confidance_interval_upper, sample_df, significance_level, samples_size

plt.figure(figsize=(20,8)).suptitle("Walmart Aged vs Purchase at 90% Confidance Interval",fontsize=14)

plt.subplot(1, 2, 1)
sns.histplot(data=df_0_17_clt_interval.sample_df, bins=20, color='#66a3ff', label='0-17 Spending', kde=True)
plt.axvline(df_0_17_clt_interval.confidance_interval_lower, color='#ff8533', linestyle='dotted', linewidth=2, label='0-17 CI Lower')
plt.axvline(df_0_17_clt_interval.confidance_interval_upper, color='#ff8533', linestyle='dotted', linewidth=2, label='0-17 CI Upper')
plt.axvline(df_0_17_clt_interval.mean, color='#ff0066', linestyle='dashed', linewidth=2, label='0-17 Mean')
plt.annotate(f'Mean: {df_0_17_clt_interval.mean:.2f}', rotation='vertical', xy=(df_0_17_clt_interval.mean, 0), xytext=(-20, 60), textcoords='offset points', color='blacK', fontsize=15)
plt.annotate(f'{df_0_17_clt_interval.confidance_interval_lower:.2f}', rotation='vertical', xy=(df_0_17_clt_interval.confidance_interval_lower, 0), xytext=(-20, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.annotate(f'{df_0_17_clt_interval.confidance_interval_upper:.2f}', rotation='vertical', xy=(df_0_17_clt_interval.confidance_interval_upper, 0), xytext=(10, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.xlabel('Spending')
plt.ylabel('Frequency')
plt.title('0-17 Aged people\'s Purchase')
plt.legend()

plt.subplot(1, 2, 2)
sns.histplot(data=df_51_55_clt_interval.sample_df, bins=20, color='#ff66ff', label='51-55 Spending', kde=True)
plt.axvline(df_51_55_clt_interval.mean, color='#0099ff', linestyle='dashed', linewidth=2, label='51-55 Mean')
plt.axvline(df_51_55_clt_interval.confidance_interval_lower, color='#8000ff', linestyle='dotted', linewidth=2, label='51-55 CI Lower')
plt.axvline(df_51_55_clt_interval.confidance_interval_upper, color='#8000ff', linestyle='dotted', linewidth=2, label='51-55 CI Upper')
plt.annotate(f'Mean: {df_51_55_clt_interval.mean:.2f}', rotation='vertical', xy=(df_51_55_clt_interval.mean, 0), xytext=(-20, 60), textcoords='offset points', color='blacK', fontsize=15)
plt.annotate(f'{df_51_55_clt_interval.confidance_interval_lower:.2f}', rotation='vertical', xy=(df_51_55_clt_interval.confidance_interval_lower, 0), xytext=(-20, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.annotate(f'{df_51_55_clt_interval.confidance_interval_upper:.2f}', rotation='vertical', xy=(df_51_55_clt_interval.confidance_interval_upper, 0), xytext=(10, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.xlabel('Spending')
plt.ylabel('Frequency')
plt.title('51-55 Aged people\'s Purchase')
plt.legend()

plt.show()


confidence_level = 0.95
sigma = 1-confidence_level #$ Significance Level


if p_value < sigma:
    print("Differance exists in Mean Purchase for All Age group.")
else:
    print("Mean Purchase for All Age group are Same.")

Differance exists in Mean Purchase for All Age group.


df_0_17_clt_interval = bootstraping(df_0_17, samples, cl=confidence_level, plot=False)
df_51_55_clt_interval = bootstraping(df_51_55, samples, cl=confidence_level, plot=False)
# mean, std_error, z_critical, margin_of_error, confidance_interval_lower, confidance_interval_upper, sample_df, significance_level, samples_size

plt.figure(figsize=(20,8)).suptitle("Walmart Aged vs Purchase at 95% Confidance Interval",fontsize=14)

plt.subplot(1, 2, 1)
sns.histplot(data=df_0_17_clt_interval.sample_df, bins=20, color='#66a3ff', label='0-17 Spending', kde=True)
plt.axvline(df_0_17_clt_interval.confidance_interval_lower, color='#ff8533', linestyle='dotted', linewidth=2, label='0-17 CI Lower')
plt.axvline(df_0_17_clt_interval.confidance_interval_upper, color='#ff8533', linestyle='dotted', linewidth=2, label='0-17 CI Upper')
plt.axvline(df_0_17_clt_interval.mean, color='#ff0066', linestyle='dashed', linewidth=2, label='0-17 Mean')
plt.annotate(f'Mean: {df_0_17_clt_interval.mean:.2f}', rotation='vertical', xy=(df_0_17_clt_interval.mean, 0), xytext=(-20, 60), textcoords='offset points', color='blacK', fontsize=15)
plt.annotate(f'{df_0_17_clt_interval.confidance_interval_lower:.2f}', rotation='vertical', xy=(df_0_17_clt_interval.confidance_interval_lower, 0), xytext=(-20, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.annotate(f'{df_0_17_clt_interval.confidance_interval_upper:.2f}', rotation='vertical', xy=(df_0_17_clt_interval.confidance_interval_upper, 0), xytext=(10, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.xlabel('Spending')
plt.ylabel('Frequency')
plt.title('0-17 Aged people\'s Purchase')
plt.legend()

plt.subplot(1, 2, 2)
sns.histplot(data=df_51_55_clt_interval.sample_df, bins=20, color='#ff66ff', label='51-55 Spending', kde=True)
plt.axvline(df_51_55_clt_interval.mean, color='#0099ff', linestyle='dashed', linewidth=2, label='51-55 Mean')
plt.axvline(df_51_55_clt_interval.confidance_interval_lower, color='#8000ff', linestyle='dotted', linewidth=2, label='51-55 CI Lower')
plt.axvline(df_51_55_clt_interval.confidance_interval_upper, color='#8000ff', linestyle='dotted', linewidth=2, label='51-55 CI Upper')
plt.annotate(f'Mean: {df_51_55_clt_interval.mean:.2f}', rotation='vertical', xy=(df_51_55_clt_interval.mean, 0), xytext=(-20, 60), textcoords='offset points', color='blacK', fontsize=15)
plt.annotate(f'{df_51_55_clt_interval.confidance_interval_lower:.2f}', rotation='vertical', xy=(df_51_55_clt_interval.confidance_interval_lower, 0), xytext=(-20, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.annotate(f'{df_51_55_clt_interval.confidance_interval_upper:.2f}', rotation='vertical', xy=(df_51_55_clt_interval.confidance_interval_upper, 0), xytext=(10, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.xlabel('Spending')
plt.ylabel('Frequency')
plt.title('51-55 Aged people\'s Purchase')
plt.legend()

plt.show()


confidence_level = 0.99
sigma = 1-confidence_level #$ Significance Level


if p_value < sigma:
    print("Differance exists in Mean Purchase for All Age group.")
else:
    print("Mean Purchase for All Age group are Same.")

Differance exists in Mean Purchase for All Age group.


df_0_17_clt_interval = bootstraping(df_0_17, samples, cl=confidence_level, plot=False)
df_51_55_clt_interval = bootstraping(df_51_55, samples, cl=confidence_level, plot=False)
# mean, std_error, z_critical, margin_of_error, confidance_interval_lower, confidance_interval_upper, sample_df, significance_level, samples_size

plt.figure(figsize=(20,8)).suptitle("Walmart Aged vs Purchase at 99% Confidance Interval",fontsize=14)

plt.subplot(1, 2, 1)
sns.histplot(data=df_0_17_clt_interval.sample_df, bins=20, color='#66a3ff', label='0-17 Spending', kde=True)
plt.axvline(df_0_17_clt_interval.confidance_interval_lower, color='#ff8533', linestyle='dotted', linewidth=2, label='0-17 CI Lower')
plt.axvline(df_0_17_clt_interval.confidance_interval_upper, color='#ff8533', linestyle='dotted', linewidth=2, label='0-17 CI Upper')
plt.axvline(df_0_17_clt_interval.mean, color='#ff0066', linestyle='dashed', linewidth=2, label='0-17 Mean')
plt.annotate(f'Mean: {df_0_17_clt_interval.mean:.2f}', rotation='vertical', xy=(df_0_17_clt_interval.mean, 0), xytext=(-20, 60), textcoords='offset points', color='blacK', fontsize=15)
plt.annotate(f'{df_0_17_clt_interval.confidance_interval_lower:.2f}', rotation='vertical', xy=(df_0_17_clt_interval.confidance_interval_lower, 0), xytext=(-20, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.annotate(f'{df_0_17_clt_interval.confidance_interval_upper:.2f}', rotation='vertical', xy=(df_0_17_clt_interval.confidance_interval_upper, 0), xytext=(10, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.xlabel('Spending')
plt.ylabel('Frequency')
plt.title('0-17 Aged people\'s Purchase')
plt.legend()

plt.subplot(1, 2, 2)
sns.histplot(data=df_51_55_clt_interval.sample_df, bins=20, color='#ff66ff', label='51-55 Spending', kde=True)
plt.axvline(df_51_55_clt_interval.mean, color='#0099ff', linestyle='dashed', linewidth=2, label='51-55 Mean')
plt.axvline(df_51_55_clt_interval.confidance_interval_lower, color='#8000ff', linestyle='dotted', linewidth=2, label='51-55 CI Lower')
plt.axvline(df_51_55_clt_interval.confidance_interval_upper, color='#8000ff', linestyle='dotted', linewidth=2, label='51-55 CI Upper')
plt.annotate(f'Mean: {df_51_55_clt_interval.mean:.2f}', rotation='vertical', xy=(df_51_55_clt_interval.mean, 0), xytext=(-20, 60), textcoords='offset points', color='blacK', fontsize=15)
plt.annotate(f'{df_51_55_clt_interval.confidance_interval_lower:.2f}', rotation='vertical', xy=(df_51_55_clt_interval.confidance_interval_lower, 0), xytext=(-20, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.annotate(f'{df_51_55_clt_interval.confidance_interval_upper:.2f}', rotation='vertical', xy=(df_51_55_clt_interval.confidance_interval_upper, 0), xytext=(10, 200), textcoords='offset points', color='blacK', fontsize=14)
plt.xlabel('Spending')
plt.ylabel('Frequency')
plt.title('51-55 Aged people\'s Purchase')
plt.legend()

plt.show()

age	8.5	21.5	30.5	40.5	48.0	53.0	55.0	All
Gender
Female	0.924068	4.477265	9.226496	4.939389	2.399522	1.798687	0.924068	24.689493
Male	1.821411	13.640495	30.693478	15.060502	5.908724	5.200630	2.985267	75.310507
All	2.745479	18.117760	39.919974	19.999891	8.308246	6.999316	3.909335	100.000000

age	8.5	21.5	30.5	40.5	48.0	53.0	55.0
Gender
Female	3.742756	18.134292	37.370130	20.006038	9.718796	7.285231	3.742756
Male	2.418535	18.112340	40.755904	19.997876	7.845816	6.905583	3.963945
All	2.745479	18.117760	39.919974	19.999891	8.308246	6.999316	3.909335

age	8.5	21.5	30.5	40.5	48.0	53.0	55.0	All
Gender
Female	33.657794	24.712021	23.112479	24.697081	28.881206	25.698034	23.637463	24.689493
Male	66.342206	75.287979	76.887521	75.302919	71.118794	74.301966	76.362537	75.310507

Gender	Female	Male
Stay_In_Current_City_Years
0	22.934756	77.065244
1	26.466688	73.533312
2	23.892849	76.107151
3	25.733326	74.266674
4+	21.948398	78.051602
All	24.689493	75.310507

Gender	Female	Male	All
Occupation
0	3.292684	9.367206	12.659889
1	3.269414	5.352429	8.621843
2	1.568715	3.264869	4.833584
3	1.439640	1.769054	3.208694
4	3.242508	9.902776	13.145284
5	0.403586	1.810140	2.213726
6	1.483453	2.216999	3.700452
7	1.823047	8.927078	10.750125
8	0.065628	0.215428	0.281056
9	1.062232	0.081444	1.143677
10	0.727728	1.622890	2.350618
11	0.272694	1.833591	2.106285
12	0.630649	5.037559	5.668208
13	0.272330	1.132587	1.404917
14	1.229484	3.735175	4.964659
15	0.434492	1.777053	2.211545
16	0.746635	3.865704	4.612339
17	0.714275	6.565370	7.279645
18	0.041813	1.162038	1.203851
19	0.366682	1.171492	1.538173
20	1.601802	4.499626	6.101427
All	24.689493	75.310507	100.000000

	User_ID	Product_ID	Gender	Age	Occupation	City_Category	Stay_In_Current_City_Years	Product_Category	Purchase
0	1000001	P00069042	F	0-17	10	A	2	3	8370
1	1000001	P00248942	F	0-17	10	A	2	1	15200
2	1000001	P00087842	F	0-17	10	A	2	12	1422
3	1000001	P00085442	F	0-17	10	A	2	12	1057
4	1000002	P00285442	M	55+	16	C	4+	8	7969

	User_ID	Gender	marital_status	Age	City_Category	Stay_In_Current_City_Years	Product_ID
count	550068	550068	550068	550068	550068	550068	550068
unique	5891	2	2	7	3	5	3631
top	1001680	Male	Single	26-35	B	1	P00265242
freq	1026	414259	324731	219587	231173	193821	1880

	age	Occupation	Product_Category	Purchase
count	550068.000000	550068.000000	550068.000000	550068.000000
mean	34.251962	8.076707	5.404270	9263.968713
std	10.873372	6.522660	3.936211	5023.065394
min	8.500000	0.000000	1.000000	12.000000
25%	30.500000	2.000000	1.000000	5823.000000
50%	30.500000	7.000000	5.000000	8047.000000
75%	40.500000	14.000000	8.000000	12054.000000
max	55.000000	20.000000	20.000000	23961.000000

	count	mean	std	min	25%	50%	75%	max
Gender
Female	135809.0	6.74054	6.239639	0.0	1.0	4.0	11.0	20.0
Male	414259.0	8.51475	6.553790	0.0	3.0	7.0	15.0	20.0

Gender	Female	Male	All
marital_status
Partnered	10.360174	30.605125	40.965299
Single	14.329319	44.705382	59.034701
All	24.689493	75.310507	100.000000

Gender	Female	Male
marital_status
Partnered	25.290121	74.709879
Single	24.272706	75.727294
All	24.689493	75.310507

Gender	Female	Male	All
marital_status
Partnered	41.961873	40.638586	40.965299
Single	58.038127	59.361414	59.034701

Gender	Female	Male	All
City_Category
A	6.490834	20.364028	26.854862
B	10.507065	31.519194	42.026259
C	7.691594	23.427285	31.118880
All	24.689493	75.310507	100.000000

Gender	Female	Male
City_Category
A	24.170051	75.829949
B	25.001190	74.998810
C	24.716810	75.283190
All	24.689493	75.310507

Gender	Female	Male	All
City_Category
A	26.289863	27.040088	26.854862
B	42.556826	41.852319	42.026259
C	31.153311	31.107592	31.118880

Gender	Female	Male	All
Stay_In_Current_City_Years
0	12.563969	13.840375	13.525237
1	37.772165	34.404322	35.235825
2	17.916338	18.709551	18.513711
3	18.054768	17.082308	17.322404
4+	13.692760	15.963443	15.402823

Gender	Female	Male
Occupation
0	26.008788	73.991212
1	37.920128	62.079872
2	32.454491	67.545509
3	44.866856	55.133144
4	24.666704	75.333296
5	18.231091	81.768909
6	40.088430	59.911570
7	16.958382	83.041618
8	23.350582	76.649418
9	92.878716	7.121284
10	30.959010	69.040990
11	12.946660	87.053340
12	11.126078	88.873922
13	19.384058	80.615942
14	24.764730	75.235270
15	19.646527	80.353473
16	16.187773	83.812227
17	9.811952	90.188048
18	3.473271	96.526729
19	23.838790	76.161210
20	26.252905	73.747095
All	24.689493	75.310507

Gender	Female	Male	All
Occupation
0	13.336377	12.438112	12.659889
1	13.242127	7.107148	8.621843
2	6.353776	4.335211	4.833584
3	5.830983	2.349014	3.208694
4	13.133150	13.149262	13.145284
5	1.634649	2.403569	2.213726
6	6.008438	2.943811	3.700452
7	7.383899	11.853695	10.750125
8	0.265814	0.286053	0.281056
9	4.302366	0.108145	1.143677
10	2.947522	2.154932	2.350618
11	1.104492	2.434709	2.106285
12	2.554323	6.689052	5.668208
13	1.103020	1.503890	1.404917
14	4.979788	4.959699	4.964659
15	1.759824	2.359635	2.211545
16	3.024100	5.133021	4.612339
17	2.893034	8.717735	7.279645
18	0.169355	1.542996	1.203851
19	1.485174	1.555549	1.538173
20	6.487788	5.974765	6.101427

Gender	Female	Male	All
Product_Category
1	4.514169	21.005948	25.520118
2	1.028600	3.309773	4.338373
3	1.091865	2.582772	3.674637
4	0.661555	1.475090	2.136645
5	7.628330	19.810642	27.438971
6	0.828807	2.891824	3.720631
7	0.171433	0.505028	0.676462
8	6.100700	14.610375	20.711076
9	0.012726	0.061811	0.074536
10	0.211247	0.720456	0.931703
11	0.861530	3.553742	4.415272
12	0.278511	0.439037	0.717548
13	0.265785	0.742999	1.008784
14	0.113259	0.163616	0.276875
15	0.190158	0.953337	1.143495
16	0.436673	1.350015	1.786688
17	0.011271	0.093807	0.105078
18	0.069446	0.498666	0.568112
19	0.081990	0.209429	0.291419
20	0.131438	0.332141	0.463579
All	24.689493	75.310507	100.000000

Gender	Female	Male	All
Stay_In_Current_City_Years
0	3.101980	10.423257	13.525237
1	9.325756	25.910069	35.235825
2	4.423453	14.090258	18.513711
3	4.457631	12.864773	17.322404
4+	3.380673	12.022150	15.402823
All	24.689493	75.310507	100.000000

Gender	Female	Male
Product_Category
1	17.688669	82.311331
2	23.709353	76.290647
3	29.713551	70.286449
4	30.962307	69.037693
5	27.801077	72.198923
6	22.275970	77.724030
7	25.342650	74.657350
8	29.456221	70.543779
9	17.073171	82.926829
10	22.673171	77.326829
11	19.512496	80.487504
12	38.814289	61.185711
13	26.347090	73.652910
14	40.906106	59.093894
15	16.629571	83.370429
16	24.440374	75.559626
17	10.726644	89.273356
18	12.224000	87.776000
19	28.134747	71.865253
20	28.352941	71.647059
All	24.689493	75.310507

Gender	Female	Male	All
Product_Category
1	18.283766	27.892454	25.520118
2	4.166145	4.394835	4.338373
3	4.422387	3.429497	3.674637
4	2.679498	1.958678	2.136645
5	30.897069	26.305282	27.438971
6	3.356920	3.839868	3.720631
7	0.694358	0.670595	0.676462
8	24.709703	19.400182	20.711076
9	0.051543	0.082074	0.074536
10	0.855613	0.956648	0.931703
11	3.489459	4.718787	4.415272
12	1.128055	0.582969	0.717548
13	1.076512	0.986581	1.008784
14	0.458732	0.217255	0.276875
15	0.770199	1.265875	1.143495
16	1.768660	1.792598	1.786688
17	0.045652	0.124560	0.105078
18	0.281277	0.662146	0.568112
19	0.332084	0.278087	0.291419
20	0.532365	0.441028	0.463579

Gender	Female	Male	All
Purchase_group
0-100	0.081990	0.209429	0.291419
101-500	0.129439	0.365773	0.495212
501-1K	0.334322	0.938793	1.273115
1K-2.5K	1.315292	3.323044	4.638336
2.5K-5K	2.545503	8.013009	10.558513
5K-7.5K	6.687355	17.636001	24.323356
7.5K-10K	6.516285	17.462568	23.978854
10K-12.5K	2.294625	8.608390	10.903016
12.5K-15K	0.888981	2.556593	3.445574
15K-17.5K	2.340983	9.741705	12.082688
17.5K-20K	1.041689	4.661060	5.702749
20K-22.5K	0.415221	1.464728	1.879949
22.5K-25K	0.097806	0.329414	0.427220
All	24.689493	75.310507	100.000000

	age	Occupation	Product_Category	Purchase
count	414259.000000	414259.00000	414259.000000	414259.00000
mean	34.235540	8.51475	5.301512	9437.52604
std	10.733197	6.55379	4.006275	5092.18621
min	8.500000	0.00000	1.000000	12.00000
25%	30.500000	3.00000	1.000000	5863.00000
50%	30.500000	7.00000	5.000000	8098.00000
75%	40.500000	15.00000	8.000000	12454.00000
max	55.000000	20.00000	20.000000	23961.00000

Age	0-17	18-25	26-35	36-45	46-50	51-55	55+	All
Purchase_group
0-100	0.008207	0.048762	0.099937	0.054314	0.023898	0.024381	0.018587	0.278087
101-500	0.013518	0.091006	0.181046	0.098489	0.038865	0.034278	0.028485	0.485686
501-1K	0.035726	0.199392	0.480617	0.257327	0.114663	0.094385	0.064452	1.246563
1K-2.5K	0.132767	0.831123	1.784150	0.858642	0.328297	0.294019	0.183460	4.412457
2.5K-5K	0.293295	2.045097	4.602193	2.017096	0.780912	0.582727	0.318641	10.639962
5K-7.5K	0.547725	4.134129	9.597377	4.801585	1.891329	1.564722	0.880850	23.417717
7.5K-10K	0.528896	4.112162	9.171316	4.629471	1.922469	1.715111	1.108002	23.187426
10K-12.5K	0.268190	2.102308	4.768997	2.266456	0.851400	0.763532	0.409647	11.430530
12.5K-15K	0.084971	0.618936	1.323327	0.699804	0.287743	0.254913	0.125043	3.394736
15K-17.5K	0.288708	2.319563	5.284617	2.638446	0.980787	0.930577	0.492687	12.935386
17.5K-20K	0.160769	1.203595	2.507851	1.203112	0.439097	0.447546	0.227153	6.189123
20K-22.5K	0.046589	0.352919	0.800224	0.371265	0.146044	0.149423	0.078453	1.944919
22.5K-25K	0.009173	0.053348	0.154251	0.101869	0.040313	0.049969	0.028485	0.437408
All	2.418535	18.112340	40.755904	19.997876	7.845816	6.905583	3.963945	100.000000

Purchase_group	0-100	101-500	501-1K	1K-2.5K	2.5K-5K	5K-7.5K	7.5K-10K	10K-12.5K	12.5K-15K	15K-17.5K	17.5K-20K	20K-22.5K	22.5K-25K	All
Occupation
0	0.029450	0.053831	0.149665	0.593590	1.480233	2.953949	2.779421	1.466715	0.412785	1.549272	0.709942	0.214117	0.045141	12.438112
1	0.021967	0.033312	0.099455	0.319365	0.726840	1.697247	1.811669	0.782844	0.231015	0.857435	0.361127	0.129629	0.035244	7.107148
2	0.010863	0.019312	0.051417	0.204944	0.507895	1.061896	1.024963	0.448029	0.138319	0.495584	0.249844	0.096558	0.025588	4.335211
3	0.005069	0.010380	0.033312	0.095351	0.230532	0.623523	0.556174	0.245740	0.077246	0.274466	0.137595	0.049727	0.009897	2.349014
4	0.030416	0.064211	0.142906	0.606625	1.511856	2.956604	3.063060	1.486992	0.441511	1.657659	0.870711	0.270362	0.046348	13.149262
5	0.004587	0.007483	0.024139	0.107421	0.266259	0.579589	0.499687	0.319607	0.084971	0.304158	0.165597	0.032830	0.007242	2.403569
6	0.008932	0.018346	0.037175	0.147492	0.277604	0.707287	0.732392	0.298123	0.096317	0.350023	0.186840	0.068798	0.014484	2.943811
7	0.034761	0.059383	0.140250	0.539035	1.240287	2.765661	2.706037	1.323327	0.378990	1.630622	0.768360	0.221600	0.045382	11.853695
8	0.000724	0.001448	0.001690	0.012794	0.023657	0.070005	0.063245	0.040072	0.011104	0.041761	0.016173	0.002655	0.000724	0.286053
9	0.000483	0.000000	0.001207	0.002655	0.010863	0.028726	0.026553	0.011587	0.004828	0.016173	0.003862	0.000966	0.000241	0.108145
10	0.007966	0.011346	0.032830	0.115870	0.257086	0.471686	0.473617	0.247913	0.079178	0.264086	0.149182	0.037899	0.006276	2.154932
11	0.007242	0.014725	0.033554	0.135664	0.260224	0.579106	0.591659	0.234877	0.082316	0.287743	0.139043	0.051900	0.016656	2.434709
12	0.023898	0.035002	0.085454	0.210496	0.620385	1.517891	1.529478	0.798776	0.244774	0.961959	0.491721	0.135181	0.034037	6.689052
13	0.005793	0.010139	0.026312	0.049727	0.123594	0.351471	0.423648	0.152320	0.047555	0.184667	0.086902	0.030174	0.011587	1.503890
14	0.016173	0.025105	0.064935	0.188047	0.504998	1.071069	1.133108	0.592866	0.160045	0.719598	0.342298	0.114904	0.026553	4.959699
15	0.007000	0.012311	0.024622	0.076764	0.210979	0.531552	0.579348	0.267466	0.076764	0.338436	0.173804	0.048762	0.011828	2.359635
16	0.013277	0.021001	0.061797	0.207117	0.491239	1.274082	1.198526	0.580555	0.172839	0.708011	0.288467	0.094627	0.021484	5.133021
17	0.030899	0.042968	0.119008	0.329987	0.802155	1.902916	1.871776	1.171007	0.345195	1.295808	0.623040	0.150389	0.032588	8.717735
18	0.005793	0.005311	0.021001	0.064935	0.190943	0.399991	0.301985	0.196978	0.052624	0.194806	0.078695	0.026553	0.003380	1.542996
19	0.002173	0.010139	0.024622	0.101386	0.198909	0.385508	0.342298	0.169701	0.043692	0.161735	0.075557	0.032588	0.007242	1.555549
20	0.010621	0.029933	0.071211	0.303192	0.703425	1.487958	1.478785	0.595038	0.212669	0.641386	0.270362	0.134698	0.035485	5.974765
All	0.278087	0.485686	1.246563	4.412457	10.639962	23.417717	23.187426	11.430530	3.394736	12.935386	6.189123	1.944919	0.437408	100.000000

	marital_status	Age	City_Category	Stay_In_Current_City_Years	Product_ID	Purchase_group	User_ID
count	135809	135809	135809	135809	135809	135809	135809
unique	2	7	3	5	3367	13	1666
top	Single	26-35	B	1	P00265242	5K-7.5K	1001150
freq	78821	50752	57796	51298	508	36785	752

	age	Occupation	Product_Category	Purchase
count	135809.000000	135809.000000	135809.000000	135809.000000
mean	34.302053	6.740540	5.717714	8734.565765
std	11.290097	6.239639	3.696752	4767.233289
min	8.500000	0.000000	1.000000	12.000000
25%	30.500000	1.000000	3.000000	5433.000000
50%	30.500000	4.000000	5.000000	7914.000000
75%	40.500000	11.000000	8.000000	11400.000000
max	55.000000	20.000000	20.000000	23959.000000

Age	0-17	18-25	26-35	36-45	46-50	51-55	55+	All
Purchase_group
0-100	0.018408	0.053752	0.109713	0.069951	0.036816	0.024299	0.019145	0.332084
101-500	0.022090	0.090568	0.179664	0.114131	0.047125	0.039762	0.030926	0.524266
501-1K	0.057434	0.259188	0.418234	0.290113	0.143584	0.123703	0.061852	1.354108
1K-2.5K	0.338711	1.172235	1.855547	0.949127	0.497758	0.325457	0.188500	5.327335
2.5K-5K	0.428543	2.334160	3.874559	1.942434	0.969008	0.519848	0.241516	10.310068
5K-7.5K	0.920410	5.012923	10.664242	5.149143	2.491735	1.983668	0.863713	27.085834
7.5K-10K	0.892430	4.263340	9.743095	5.327335	2.673608	2.189104	1.304037	26.392949
10K-12.5K	0.360801	1.753934	3.390791	1.939489	0.913047	0.606734	0.329139	9.293935
12.5K-15K	0.204699	0.663432	1.203160	0.815852	0.340184	0.259924	0.113395	3.600645
15K-17.5K	0.280541	1.564697	3.610954	2.069082	0.993307	0.645024	0.318094	9.481699
17.5K-20K	0.161992	0.699512	1.553653	0.899057	0.413080	0.341656	0.150211	4.219161
20K-22.5K	0.043443	0.236361	0.636188	0.349020	0.157574	0.173773	0.085414	1.681774
22.5K-25K	0.013254	0.030189	0.130330	0.091305	0.041971	0.052279	0.036816	0.396145
All	3.742756	18.134292	37.370130	20.006038	9.718796	7.285231	3.742756	100.000000

Business Case: Walmart - Confidence Interval and CLT¶

About Walmart</span>

About DataSet</span>

Business Problem</span>

Exploring The Data Set¶

Missing value detection & fill with relevent data.

Consolidated Data¶

Check for Outliers¶

Conversion of categorical attributes to 'category'.¶

Statitical Summary¶

Descriptive Statistits¶

Unique Count¶

Mean¶

Median¶

Mode¶

Uni Variate Analysis¶

User ID¶

Statitical Analysis¶

Find Probability¶

Gender¶

Statitical Analysis¶

Plot the Graph¶

Heat Map¶

Descriptive Plot¶

Marital Status¶

Statitical Analysis¶

Find Probability¶

Probability of a Gender & Marital Status across all Combination "Product ∩ Marital Status"¶

Probability of Gender's for given Marital Status "Product | Marital Status"¶

Probability of Marital Status for given Gender "Marital Status | Product"¶

Plot the Graph¶

Heat Map¶

Descriptive Plot¶

Probability Plot¶

Population Plot¶

Age¶

Statitical Analysis¶

Check for Outliers¶

Find Probability¶

Probability of a Age & Gender across all Combination "Age ∩ Gender"¶

Probability of Age's for given Gender "Age | Gender"¶

Probability of Gender for given Age "Gender | Age"¶

Plot the Graph¶

Heat Map¶

Descriptive Plot¶

Probability Plot¶

Population Plot¶

City Category¶

Statitical Analysis¶

Find Probability¶

Probability of a Gender & City Category across all Combination "Gender ∩ City Category"¶

Probability of Gender's for given City Category "Gender | City Category"¶

Probability of City Category for given Gender "City Category | Product"¶

Plot the Graph¶

Heat Map¶

Descriptive Plot¶

Probability Plot¶

Population Plot¶

Stay In Current City Years¶

Statitical Analysis¶

Find Probability¶

Probability of a Gender & Stay In Current City Years across all Combination "Gender ∩ Stay In Current City Years"¶

Probability of Gender's for given Stay In Current City Years "Gender | Stay In Current City Years"¶

Probability of Stay In Current City Years for given Gender "Stay In Current City Years | Product"¶

Plot the Graph¶

Heat Map¶

Descriptive Plot¶

Probability Plot¶

Population Plot¶

Occupation¶

Statitical Analysis¶

Check for Outliers¶

Find Probability¶

Probability of a Gender & Occupation across all Combination "Gender ∩ Occupation"¶

Probability of Gender's for given Occupation "Gender | Occupation"¶

Probability of Occupation for given Gender "Occupation | Product"¶

Plot the Graph¶

Heat Map¶

Descriptive Plot¶

Probability Plot¶

Descriptive `Statistits`¶

`Heat Map`¶

`Descriptive` Plot¶

Probability of a `Gender` & `Marital Status` across all Combination "Product ∩ Marital Status"¶

Probability of `Gender's` for given `Marital Status` "Product | Marital Status"¶

Probability of `Marital Status` for given `Gender` "Marital Status | Product"¶

`Heat Map`¶

`Descriptive` Plot¶

`Probability` Plot¶

`Population` Plot¶

Probability of a `Age` & `Gender` across all Combination "Age ∩ Gender"¶

Probability of `Age's` for given `Gender` "Age | Gender"¶

Probability of `Gender` for given `Age` "Gender | Age"¶

`Heat Map`¶

`Descriptive` Plot¶

`Probability` Plot¶

`Population` Plot¶

Probability of a `Gender` & `City Category` across all Combination "Gender ∩ City Category"¶

Probability of `Gender's` for given `City Category` "Gender | City Category"¶

Probability of `City Category` for given `Gender` "City Category | Product"¶

`Heat Map`¶

`Descriptive` Plot¶

`Probability` Plot¶

`Population` Plot¶

Probability of a `Gender` & `Stay In Current City Years` across all Combination "Gender ∩ Stay In Current City Years"¶

Probability of `Gender's` for given `Stay In Current City Years` "Gender | Stay In Current City Years"¶

Probability of `Stay In Current City Years` for given `Gender` "Stay In Current City Years | Product"¶

`Heat Map`¶

`Descriptive` Plot¶

`Probability` Plot¶

`Population` Plot¶

Probability of a `Gender` & `Occupation` across all Combination "Gender ∩ Occupation"¶

Probability of `Gender's` for given `Occupation` "Gender | Occupation"¶

Probability of `Occupation` for given `Gender` "Occupation | Product"¶

`Heat Map`¶

`Descriptive` Plot¶

`Probability` Plot¶

`Population` Plot¶

Probability of a `Gender` & `Product Category` across all Combination "Gender ∩ Product Category"¶

Probability of `Gender's` for given `Product Category` "Gender | Product Category"¶

Probability of `Product Category` for given `Gender` "Product Category | Product"¶

`Heat Map`¶

`Descriptive` Plot¶

`Probability` Plot¶

`Population` Plot¶

Probability of a `Gender` & `Purchase` across all Combination "Gender ∩ Purchase"¶

Probability of `Gender's` for given `Purchase` "Gender | Purchase"¶

Probability of `Purchase` for given `Gender` "Purchase | Product"¶

`Heat Map`¶

`Descriptive` Plot¶

`Probability` Plot¶

`Population` Plot¶

Profile of Male¶

Profile of Female¶

Purchase_group	0-100	101-500	501-1K	1K-2.5K	2.5K-5K	5K-7.5K	7.5K-10K	10K-12.5K	12.5K-15K	15K-17.5K	17.5K-20K	20K-22.5K	22.5K-25K	All
Occupation
0	0.048598	0.071424	0.184082	0.651650	1.304774	3.583709	3.644825	1.233350	0.481559	1.268694	0.534574	0.254033	0.075105	13.336377
1	0.035344	0.061852	0.191445	0.715711	1.382088	3.737602	3.652188	1.162662	0.447688	1.147199	0.440324	0.206908	0.061115	13.242127
2	0.012518	0.034607	0.080996	0.354910	0.683313	1.792959	1.805477	0.557400	0.195863	0.511012	0.201018	0.100877	0.022826	6.353776
3	0.020617	0.027244	0.076578	0.312203	0.605262	1.487383	1.461612	0.538992	0.226053	0.622197	0.315885	0.114131	0.022826	5.830983
4	0.038289	0.077314	0.183346	0.777563	1.521254	3.554993	3.300223	1.251022	0.492604	1.178125	0.533102	0.192918	0.032398	13.133150
5	0.003682	0.009572	0.021354	0.078051	0.195863	0.441061	0.408662	0.150947	0.045652	0.155365	0.090568	0.029453	0.004418	1.634649
6	0.019145	0.032398	0.063324	0.248143	0.575072	1.523463	1.668520	0.614834	0.206908	0.667850	0.248879	0.120022	0.019881	6.008438
7	0.035344	0.036816	0.095723	0.298949	0.619252	2.044047	1.960842	0.709820	0.239307	0.801861	0.382154	0.131803	0.027980	7.383899
8	0.001473	0.000000	0.003682	0.017672	0.038289	0.041971	0.057434	0.039762	0.005154	0.042707	0.014727	0.001473	0.001473	0.265814
9	0.014727	0.025771	0.053752	0.244461	0.405717	1.278266	1.041904	0.422652	0.198809	0.378473	0.178191	0.051543	0.008100	4.302366
10	0.016199	0.019145	0.044180	0.298213	0.360065	0.720865	0.659014	0.280541	0.161992	0.226053	0.125176	0.028717	0.007363	2.947522
11	0.006627	0.005154	0.019145	0.036816	0.086150	0.301895	0.324721	0.098668	0.033871	0.106031	0.053752	0.025035	0.006627	1.104492
12	0.014727	0.013990	0.027980	0.127385	0.250352	0.622197	0.672268	0.259188	0.105295	0.246670	0.150947	0.050807	0.012518	2.554323
13	0.003682	0.012518	0.023563	0.058170	0.064797	0.284959	0.403508	0.083941	0.030189	0.073633	0.040498	0.015463	0.008100	1.103020
14	0.017672	0.022826	0.052279	0.211326	0.508066	1.519781	1.311401	0.466096	0.153893	0.467568	0.167883	0.072160	0.008836	4.979788
15	0.005154	0.008836	0.027980	0.058906	0.128121	0.467568	0.487449	0.164201	0.077314	0.190709	0.086150	0.043443	0.013990	1.759824
16	0.006627	0.023563	0.047125	0.143584	0.335029	0.729701	0.804807	0.275387	0.114867	0.332820	0.145057	0.055961	0.009572	3.024100
17	0.012518	0.011781	0.039025	0.127385	0.267287	0.697303	0.705402	0.297477	0.096459	0.361537	0.195127	0.064797	0.016936	2.893034
18	0.000736	0.001473	0.000736	0.006627	0.011781	0.030189	0.056697	0.018408	0.005154	0.016199	0.011781	0.005891	0.003682	0.169355
19	0.002945	0.005891	0.022090	0.106031	0.195863	0.379209	0.367428	0.129594	0.050807	0.127385	0.066270	0.026508	0.005154	1.485174
20	0.015463	0.022090	0.095723	0.453578	0.771672	1.846711	1.598569	0.538992	0.231207	0.559609	0.237098	0.089832	0.027244	6.487788
All	0.332084	0.524266	1.354108	5.327335	10.310068	27.085834	26.392949	9.293935	3.600645	9.481699	4.219161	1.681774	0.396145	100.000000

	Age	Purchase
5	51-55	9534.808031
6	55+	9336.280459
3	36-45	9331.350695
2	26-35	9252.690633
4	46-50	9208.625697
1	18-25	9169.663606
0	0-17	8933.464640